\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art novel imaging techniques by focusing on the most important evidence-based developments in this area.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"d9159ce31733bf78cc2a79b18c225994",bookSignature:"Dr. Gabriel Cismaru",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11867.jpg",keywords:"Hypertrophic Cardiomyopathy, Dilated Cardiomyopathy, Restrictive Cardiomyopathy, Transesophageal Echocardiography, Intracardiac Echocardiography, 3-Dimensional Echocardiography, Adult Congenital Heart Disease, Tetralogy of Fallot, Transposition of the Great Vessels, Coronary Artery Disease, Risk Stratification, Revascularization",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 21st 2022",dateEndSecondStepPublish:"May 19th 2022",dateEndThirdStepPublish:"July 18th 2022",dateEndFourthStepPublish:"October 6th 2022",dateEndFifthStepPublish:"December 5th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Cismaru Gabriel is an Assistant Professor at the University of Medicine and Pharmacy Cluj-Napoca, certified in Cardiology. 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He has authored or co-authored peer-reviewed articles and book chapters in the field of cardiac pacing, defibrillation, electrophysiological study, and catheter ablation.",coeditorOneBiosketch:"Raluca Tomoaia is an MD, Ph.D. in novel techniques in Echocardiography at the University of Medicine and Pharmacy in Cluj-Napoca, Romania., assistant professor, and a researcher in echocardiography and cardiovascular imaging.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"191888",title:"Dr.",name:"Gabriel",middleName:null,surname:"Cismaru",slug:"gabriel-cismaru",fullName:"Gabriel Cismaru",profilePictureURL:"https://mts.intechopen.com/storage/users/191888/images/system/191888.png",biography:"Dr. Cismaru Gabriel is an assistant professor at the Cluj-Napoca University of Medicine and Pharmacy, Romania, where he has been qualified in cardiology since 2011. 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1. Introduction
The advent of digital technologies in the healthcare field is characterized by continual challenges in both application and practicality. Unification of disparate health systems have been slow and the adoption of a fully integrated healthcare system in most parts of the world has not been accomplished. The inherent nature and complexity of human biology, as well as the variation between individual patients has consistently shown the importance of the human element in diagnosing and treating diseases. However, advances in digital technologies are no doubt becoming indispensable tools for healthcare professionals in providing the best care for patients.
The improvement of data technologies, including storage size, computational power, and data transfer speeds, has enabled the widespread adoption of machine learning in many fields—healthcare included. Due to the multivariate nature of providing quality healthcare to an individual, the recent trends in medicine have emphasized the need for a personalized medicine or “precision medicine” approach to healthcare. The goal of personalized medicine is to use large amounts of healthcare data to find, predict, and analyze diagnostic decisions, which physicians can in turn implement for each individual patient. Such data includes but is not limited to genetic or familial information, medical imaging data, drug combinations, population wide patient health outcomes, and natural language processing of existing medical documentation.
We will focus primarily on three of the largest applications of machine learning (ML) in the medical and biomedical fields. As a rapidly evolving field, there is a wide range of potential applications of machine learning in the healthcare field which may encompass auxiliary aspects of the field such as personnel management, insurance policies, regulatory affairs, and much more. As such, the topics covered in this chapter have been narrowed down to three common applications of machine learning.
The first is the use of machine learning in medical images such as magnetic resonance imaging (MRIs), computerized axial tomography (CAT) scans, ultrasound (US) imaging, and positron emission tomography (PET) scans. The result of these imaging modalities is a set or series of images which typically requires a radiologist to interpret and make a diagnosis. ML techniques have rapidly been advancing to predict and find images which may indicate a disease state or serious issue.
The second is natural language processing of medical documents. With the push towards electronic medical records (EMR) in many countries, the consensus from many healthcare professionals has been that the process is slow, tedious, and, in many cases, completely botched. This can sometimes lead to poorer overall healthcare for patients. One of the major challenges is the amount of physical medical records and documentation that already exists in many hospitals and clinics. Different formatting, hand-written notes, and a plethora of incomplete or non-centralized information has made the switch to adopting electronic medical records less than efficient.
The third machine learning application encompasses the use of human genetics to predict disease and find causes of disease. With the advent of next-generation sequencing (NGS) techniques and the explosion of genetic data including large databases of population-wide genetic information, the attempt to discern meaningful information of how genetics may affect human health is now at the forefront of many research endeavors. By understanding how complex diseases may manifest and how genetics may increase or decrease an individual person’s risk can aid in preventative healthcare. This could provide physicians with more information on how to tailor a specific patients’ care plan to reduce the risk of acquiring more complex diseases.
The common issue present in all three of these topics is how to translate health data acquired from the Internet of Things, into understandable, useful, trustworthy information for patients and clinician. How do we interpret hundreds of thousands of inputs and parameters from the data? How do we do this efficiently? What is the progress of addressing this problem currently?
2. Artificial intelligence and machine learning
Artificial intelligence (AI) has been intricately linked to the rise of modern-day computing machines. Machine learning has its roots and beginnings firmly planted in history. Alan Turing’s work in cracking the German Enigma machine during World War II became the basis for much of modern computer science. The Turing Test, which aims to see if AI has become indistinguishable from human intelligence, is also named after him [1, 2].
At the height of the Second World War, the Allies had a significant logistical hurdle in the Atlantic. The United States and United Kingdom needed to set up secure shipping lines to move both armaments and troops to England in preparation for a mainland European invasion. However, the German U-boats were extremely effective at disrupting and sinking many of the ships traversing these shipping lanes [3]. As such, the Allies needed to intercept German communications to swing the Battle of the Atlantic in their favor. The Germans encrypted their communications with The Enigma Machine, the most sophisticated encryption device of its time.
Turing and the rest of Bletchley Park were tasked with breaking the coded messages produced by The Enigma Machine and eventually produced The Bombe, a mechanical computing device which successfully decoded the cipher of The Enigma machine (Figure 1). Using the Bombe, they read the German orders sent to submarines and navigated their ships around these dangers. This was Turing’s first intelligent machine. Alan Turing would later go on to describe the idea of a thinking machine which would eventually be called AI [4].
Figure 1.
Picture of the German Enigma machine which was used to code military communications. Taken from Wikimedia Commons.
Machine learning is a subset of AI and the term was coined in the late 1950s by Arthur Samuel who published a paper on training computers to play checkers when he worked with IBM [5]. AI is best described as giving human-like intelligence to machines in a manner that directly mimics the decision making and processing of the human conscience. ML is the subset of AI that focuses on giving machines the ability to learn in an unaided manner without any human intervention.
By the late 1960s, researchers were already trying to teach computers to play basic games such as tic-tac-toe [6]. Eventually, the idea of neural networks, which were based on a theoretical model of human neuron connection and communication, was expanded into artificial neural networks (ANNs) [7, 8]. These foundational works laid dormant for many years due to the impracticality and poor performance of the systems created. Computing technology had not yet advanced enough to reduce the computational time to a practical level.
The modern computer era led to exponential increases in both computational power and data storage capacity. With the introduction of IBM’s Deep Blue and Google’s AlphaGo in recent decades, several leaps in AI have shown the capacity of AI to solve real world, complex problems [9, 10]. As such, the promise of machine learning has taken hold in almost every sector imaginable.
The widespread adoption of machine learning can be mostly attributed to the availability of extremely large datasets and the improvement of computational techniques, which reduce overfitting and improve the generalization of trained models. These two factors have been the driving force to the rapid popularization and adoption of machine learning in almost every field today. This coupled with the increasing prevalence of interconnected devices or the Internet of Things (IoT) has created a rich infrastructure upon which to build predictive and automated systems.
Machine learning is a primary method of understanding the massive influx of health data today. An infrastructure of systems to complement the increasing IoT infrastructure will undoubtedly rely heavily on these techniques. Many use cases have already show enormous promise. How do these techniques work and how do they give us insight into seemingly unconnected information?
2.1 Machine learning algorithms
Machine learning is broadly split into supervised and unsupervised learning. Algorithms falling under both categories implement mathematical models. Each algorithm aims to give computers the ability to learn how to perform certain tasks.
2.1.1 Supervised learning
Supervised learning typically employs training data known as labeled data. Training data has one or more inputs and has a “labeled” output. Models use these labeled results to assess themselves during training, with the goal of improving the prediction of new data (i.e., a set of test data) [11]. Typically, supervised learning models focus on classification and regression algorithms [12]. Classification problems are very common in medicine. In most clinical settings, diagnosing of a patient involves a doctor classifying the ailment given a certain set of symptoms. Regression problems tend to look at predicting numerical results like estimated length of stay in a hospital given a certain set of data like vital signs, medical history, and weight.
Common algorithms included in this supervised learning group are random forests (RF), decision trees (DT), Naïve Bayes models, linear and logistic regression, and support vector machines (SVM), though neural networks can also be trained through supervised learning [13]. Random forests are a form of decision trees but are an ensemble set of independently trained decision trees. The resulting predictions of the trees are typically averaged to get a better end result and prediction [14]. Each tree is built by using a random sample of the data with replacement and at each candidate split a random subset of features are also selected. This prevents each learner or tree from focusing too much on apparently predictive features of the training set which may not be predictive on new data. In other words, it increases generalization of the model. Random forests can have hundreds or even thousands of trees and work fairly well on noisy data [15]. The model created from aggregating results from multiple trees trained on the data will give a prediction that can be assessed using test data (Figure 2).
Figure 2.
Example of a workflow for training and assessing a random forest model. Each green triangle represents an independently trained tree from the training data. The prediction of each tree is summed and is represented as the model. Test data is then fed to the model, i.e., all the trees, and the resulting prediction is made. The prediction is then compared to the original test data to assess how the model performs.
A method used to improve many supervised algorithms is known as gradient boosting. Taking decision trees as an example, the gradient boosting machine as it is commonly known, performs a similar ensemble training method as the random forest but with “weak learners.” Instead of building the decision trees in parallel as in the random forest algorithm, the trees are built sequentially with the error of the previous tree being used to improve the next tree [16]. These trees are not nearly as deep as the random forest trees, which is why they are called “weak” (Figure 3). Typically, better results can be achieved with gradient boosting, but tuning is much more difficult, and the risk of overfitting is higher. Gradient boosting works well with unbalanced data and training time is significantly faster due to the gradient descent nature of the algorithm [17, 18].
Figure 3.
Example of a simple workflow for training and assessing a gradient boosting machine model. Each green triangle represents a trained tree from the training data with the subsequent tree using the residuals or errors from the prior tree to improve its prediction. The prediction of each tree is summed and is represented as the model. Test data is then fed to the model, i.e., all the trees, and the resulting prediction is made. The prediction is then compared to the original test data to assess how the model performs.
2.1.2 Unsupervised learning
Unsupervised machine learning uses unlabeled data to find patterns within the data itself [19]. These algorithms typically excel at clustering data into relevant groups, allowing for detection of latent characteristics which may not be immediately obvious. However, they are also more computationally intensive and require a larger amount of data to perform.
The most common and well-known algorithms are K-means clustering and deep learning, though deep learning can be used in a supervised manner [12, 20]. Such algorithms also perform association tasks which are similar to clustering. These algorithms are considered unsupervised because there is no human input as to what set of attributes the clusters will be centered on.
The typical k-means algorithm has several variations such as k-medians and k-medoids, however the principle is the same for each algorithm. The algorithm uses Euclidian distance to find the “nearest” center or mean for a cluster assuming there are k clusters. It then assigns the current data point to that cluster and then recalculates the center for the cluster, updating it for the next data point [21]. The biggest drawback to this algorithm is that it must be initialized with an expected number of “means” or “centers.” Improper selection of the k value can result in poor clustering.
Deep learning uses neural nets to perform predictions even on unlabeled data as well as classification techniques. Based off models of human neurons, perceptrons, as they are typically called, are organized into many networked layers making the network “deep” in nature [20]. Each perceptron has multiple inputs and a single output. They are organized into layers where the outputs of the previous layer serve as the inputs for the next layer. The input layer requires one perceptron per input variable and the subsequent layers are determined before training by a human (Figure 4). This is one of the difficulties and challenges in building an effective neural net. The computationally intensive nature of computing each perceptron for a large neural net can mean that training alone can take days to weeks for large data sets [22].
Figure 4.
Example of a simple neural net with two hidden layers of three perceptrons each. The number of inputs, number of hidden layers, and number of perceptrons in each layer can be changed. Additionally, the connections between layers and perceptrons can also be changed.
2.1.3 Hyperparameters
In machine learning, a model typically has a set of parameters as well as a set of hyperparameters. Parameters are variables about the model that can be changed during training. For example, parameters can be the values of the training data itself with each piece of data being different along one or several of the parameters. Whereas hyperparameters are typically set before training occurs and cannot change once learning begins. Hyperparameters typically are set to tune values like the model’s learning speed and constrain the algorithm itself.
Different algorithms will have different sets of hyperparameters. For example, a common hyper parameter for artificial neural networks is the number of hidden layers. Additionally, a separate but related hyperparameter is the number of perceptrons in each hidden layer. Whereas a similar equivalent in decision trees would be the maximum number of leaves in a tree or the maximum depth for a tree. Other common hyperparameters include learning rate, batch size, dropout criterion, and stopping metric.
Properly selecting hyperparameters can significantly speed up the search for a proper generalized model without sacrificing performance. However, in many cases finding the proper set is more of an art than a science. Many researchers have attempted to make hyperparameter searching a more efficient and reproducible task [23, 24, 25]. Again, this process also highly depends on the algorithm, dataset, and problem you are trying to solve. A machine learning model can be tuned a nearly infinite amount of different ways to achieve better performance. Hyperparameters represent a way to reproduce results and also serve as a tool to properly validate models.
2.1.4 Algorithm principles
Considering the pace of research in the field, there are constant advances and improvements to many of these machine learning techniques, but the important thing to remember is that not all algorithms work for all use cases. Each algorithm has advantages and disadvantages. Certain data types may also affect the performance of individual algorithms and the time spent implementing such models will often be a result of testing different variations, parameters, and hyperparameters within these algorithms to achieve the best generalized performance.
2.2 Assessment of model performance
The goal of any machine learning algorithm is to utilize real data to create a model that performs the best on real-world scenarios, and that can be assessed in a quantitative, reproducible manner. Assessment of statistical models is a whole subfield in itself, but we will briefly discuss the basics, which are applicable for almost any machine learning algorithm you will come across.
2.2.1 Sensitivity vs. specificity
Sensitivity and specificity are two important metrics used in a statistical or machine learning model to assess if the model is performing successfully. As such, it is important to understand what each of these numbers tell us about what a trained model can do, and what the model cannot do.
Sensitivity is the probability that a positive result occurs given that the sample is indeed positive. Mathematically,
Sensitivity=Number of True PositivesNumber of True Positives+Number of False Negatives
This is also sometimes referred to as the recall or hit rate, or just simply the true positive rate, and the sensitivity is equivalent to 1−False Negative Rate.
Specificity is the probability of a negative result given that the sample is negative. Mathematically,
Specificity=Number of True NegativesNumber of True Negatives+Number of False Positives
This value is also referred to as the selectivity of the test. This is equivalent to 1−False Positive Rate.
2.2.2 The receiver operator curve and area-under the curve
The standard metric for assessing the performance of machine learning models is known as the receiver operating characteristic (ROC). The ROC can be summarized by a number from 0 to 1, which is the measured area-under-the-ROC curve (AUC). The ROC curve is a 2D plot that measures the false positive rate vs. true positive rate. There are four numbers that are used to determine the effectiveness of a test: true positive rate, false positive rate, true negative rate, and false negative rate.
True positive and true negative are the correct answers to a test while false positive and false negative are incorrect answers to the test or model. These numbers can be condensed further into two numbers known as sensitivity and specificity. We have already discussed sensitivity and specificity but now we will discuss how they are used to create the ROC.
Ideally a test would have both high sensitivity and high specificity. However, there is a tradeoff, prioritizing one often leads to the detriment of the other. When setting the threshold low, one will receive a high true positive rate (high sensitivity) and a high false positive rate (low specificity). Conversely, setting the threshold high will result in a low true positive rate (low sensitivity) and a low false positive rate (high specificity).
The ROC and AUC metric is used to characterize most of the classification tasks many machine learning models are attempting to do; does this person have the disease or do they not? If a test has a high sensitivity and a high specificity it is considered a near perfect test and the AUC is close to 1 (Figure 5). If the test is random then the AUC is 0.5. The x-axis is typically the false positive rate (or 1 – specificity). Ideally, the false positive rate is as low as possible. The y-axis is typically the true positive rate (sensitivity). The sensitivity is what is usually maximized. On a typical curve, the midpoint of the curve is the most balanced trade-off between sensitivity and specificity though this is not always the case. The AUC value is a simpler, more generalized way, to assess the performance rather than the varying tradeoffs between sensitivity and specificity.
Figure 5.
Examples of an AUC denoting a model which has good predictive power (left) and an AUC denoting a model with poor or near random predictive power (right). 1 – Specificity is sometimes written as false positive rate (fpr) and sensitivity can be read as true positive rate (tpr).
Another way to think of AUC is as a percentage the model can correctly identify and separate a positive result from a negative result. Given an unknown case, a model with an AUC of 0.75 has a 75% chance of correctly identifying whether the case is a positive case or a negative case. This number will quickly tell you the results of any model.
2.2.3 Overfitting
Overfitting is one of the main concerns when training any model [26]. Simply put, when training a model on a set of data, over-training the model will improve the performance of the model on that specific dataset but at the cost of losing generalization to other datasets. An overfitted model will not work when applied to new data it has never seen before. From a practical standpoint, such a model is not very useful in a real-world application.
When training any machine learning model, the ideal result is a generalized model. A generalized model works well on a variety of different cases and a variety of different datasets, especially data it has never seen before. As such, many researchers are hesitant to give too much credence to a model or method that utilizes a single dataset.
A variety of methods have been used to prevent models from overfitting and many of these are now encapsulated in the hyperparameters discussed earlier. The idea is to prevent the models from adapting too quickly to the dataset it is being trained on. This subset of methods is known as regularization [27].
One such method, used in neural nets, is called dropout. This method is widely used to prevent artificial neural nets from overfitting during classification tasks. The method is fairly simple. During the training process, random perceptrons and their corresponding connections are “dropped” from the network. These “thinned” networks have better performance compared to other regularization techniques on supervised learning tasks [28].
Often a method known as cross-validation is used to assess the performance and validate the generalized predictive ability of a model. The most common method for building machine learning models is the partitioning of the data set into roughly 80% for training and 20% for testing. This partition is typically less useful for linear models but splitting is more beneficial for complex models [29]. During cross-validation, this split is done in separate sections of the data to ensure proper coverage. For example, if a 10-fold cross-validation is performed, the first split in a data set with 100 observations could be a the first 80 for training and the last 20 for test, the second split could be the first 10 and last 10 for test and the middle 80 for training, etc. (Figure 6). This creates 10 models using the same algorithm just trained and tested on different portions of the same data. The average performance of these 10 models gives a good measurement of the generalized performance of the algorithm on that type of data.
Figure 6.
Example of a set of cross validation splits. There are n splits for the number of iterations desired and the results of all iterations are averaged to assess the generalized performance of a model trained on a dataset.
2.3 Big data and the health information explosion
The healthcare sector has always had a very large amount of information, often times stored as physical documents in clinics, hospitals, regulatory agencies, and biomedical companies [30, 31]. With the push to electronic medical records (EMR), this information is rapidly being transformed into a form which can be leveraged by AI technologies. The estimated amount of healthcare data stored in 2011 was around 150 exabytes (1 EB = 1018 bytes), though that number is most likely exponentially larger almost a decade later [32, 33]. These large databases, when in a digitized form, are often known as Big Data.
However, such healthcare information is very different in both form and function. Visual data in the form of medical images is very different than familial history which may be simple text-based information. Laboratory and clinical tests may be reported as numbers only, while health outcomes are often qualitative in nature and may be a simple yes or no entry in a spreadsheet. Insurance and administrative data is also indirectly linked to various information, such as patient outcomes, while information from sensor based technologies like EKGs, pulse oximeters, and EEG provide time-series data of vital signs [34].
Additionally, the genomic revolution has contributed enormously to the data explosion. Large-scale genetic databases such as the Cancer Genome Atlas (TCGA) and the UK Biobank include thousands of patients’ genetic sequencing information along with various other health information such as disease state, age of diagnosis, time of death, and much more [35, 36, 37, 38]. Copy number variation (CNV) data from the UK Biobank’s roughly 500,000 patients, which does not even contain the raw sequence reads, is almost 2 Terabytes (TB) alone in flat text files. These genetic databases rely on an array of assays and sequencers spread across different hospitals and research facilities around the globe, before being processed and transferred to their respective centralized storage databases [35, 39, 40].
The collection of biological data and creation of these databases show no evidence of slowing down. Many biobanks, databases which contain some form of biological samples such as blood or serum, contain thousands of participants and many have plans to collect hundreds of thousands of samples from patients (Table 1). Because many databases are growing so quickly it is unclear how much data resides in many of these databases. However, The Cancer Genome Atlas alone contains 2.5 petabytes (1 PB = 1015 bytes) of data and the UK Biobank contains 26 terabytes (1 TB = 1012 bytes) of just genetic information (UK Biobank also contains medical images such as brain scans which is not included in this table).
Number represents genetic data only. Project or study may also include unreported data including medical images and health records.
Implementing machine learning systems into a hospital with this complex information Web is usually slow, due to the abundance of caution needed to ensure patient health. Many physicians are also wary of adopting new systems that are unproven in a clinical setting due to the risk of litigation and potentially catastrophic consequences for their patients.
3. Machine learning of medical images
Modern medical images are digital in nature. To effectively utilize them in healthcare there are several challenges that must be overcome. Medical imaging describes a collection of techniques to create visual representations of interior portions of the human body for the purpose of diagnosis, analysis, and medical intervention. This is beneficial in avoiding or reducing the need for the older clinical standard of exploratory surgery. Since opening any portion of the human body through surgical means increasing the risk of infections, strokes, and other complications, medical imaging is now the preferred tool for initial diagnosis in the clinical setting.
The current clinical standard of assessing medical images is the use of trained physicians, pathologists, or radiologists who examine the images and determine the root cause of clinical ailments. This clinical standard is prone to human error and is also costly and expensive, often requiring years or decades of experience to achieve a level of understanding which can consistently assess these images. Considering that the demonstration of viable machine learning capabilities in the modern age was demonstrated by Andrew Ng using images pulled from YouTube videos, it is clear why medical images were one of the first areas addressed during the initial adoption of machine learning techniques in healthcare [54].
Accuracy of diagnosis is extremely important in the medical field as improper diagnosis could lead to severe consequences and results. If a surgery is performed where none was needed or a misdiagnosis leads to improper dosages of prescribed medication, the possibility of a fatal outcome increases. In the realm of image processing, most techniques rely fundamentally on deep learning (DL) and specifically in artificial neural networks (ANNs). Modern techniques utilize improvements to ANNs in the form of convolutional neural networks (CNNs) to boost performance when classifying images.
The majority of the current publications are using some form of CNNs when it comes to object detection in medical images [55]. Graphic-processing unit (GPU) acceleration has made the building of deep CNNs more efficient, however significant challenges in creating a competent model still exist. The biggest issue is the need for a large amount of annotated medical image data. The cost to aggregate and create such databases is often prohibitive since it requires trained physicians’ time to annotate the images. Additionally, concerns involving patient privacy often hinders the ability to make such databases open-source. Many studies only use around 100–1000 samples in training CNNs. This limited sample size increases the risk of overfitting and reduces the accuracy of the predictions [56].
Concerns regarding the implementation of machine learning into clinical diagnosis have been raised regarding proper validation of models [57]. The main fears entail properly scoping the intended goals of a machine learning model, reducing dimensionality of the data, and reproducibility of training such models on real-world and new clinical data. Validating results on other datasets can be difficult due to the lack of larger datasets for niche diseases, where the aggregation of this data can take more work than the actual training of the model. Medical imaging data is inherently more difficult to acquire and is more difficult to store and process. The infrastructure to handle the data has simply not kept up with the increase in the amount of data.
3.1 Lesion detection and computer automated detection
The most common use of current machine learning technologies in medicine is for computer automated detection (CAD) specifically in the detection of lesions such as those commonly found in mammograms, brain scans, and other body scans [58]. These methods use CNNs to arrive at the probability that a candidate lesion is in fact a lesion, often utilizing several 2D slices of 3D rotational scans of either CAT or MRI images.
Ultrasound images are also used in training and a variety of methods such as randomized rotation of the images or centering candidate lesions in the center of the image. Especially in mammography, CAD techniques have reached a level where they are used as a “second opinion” for most radiologists, greatly improving the accuracy of screenings without doubling the cost associated with using a human as the “second opinion” Figure 7.
Figure 7.
Example of mammogram with the left image being that of a raw mammogram and the right hand being the image with the detection overlaid with the region of interest in white, using NASA software originally used to enhance earth science imagery. Taken from NASA press release, credited to Bartron Medical Imaging.
CAD is also currently split into detection and diagnosis. This distinction is subtle but important. A lesion can be categorized as either benign or malignant, based off a physician’s knowledge and assessment. However, the actual detection is a crucial first step in treating a patient.
Computer aided detection is the actual recognition of potential lesions from a medical image. For example, detection and segmentation of glioblastoma is a difficult task, due to the invasive and widespread nature of these tumors. Unlike other brain tumors, they are not easily localized and assessing how treatments such as chemotherapy are performing is in itself a difficult task. Deep learning has aided in this by helping automate assessment of glioblastoma MRIs [59].
Computer aided diagnosis describes the probability a lesion is malignant in nature. These methods are primarily used to improve the accuracy of diagnosis and improve early diagnosis in the clinical setting. Again, these tasks have consistently been performed by machine learning especially in brain related applications, due to the difficult nature of assessing brain health. Additionally, diagnosis of Alzheimer’s through medical imaging is a possible application for deep learning which is showing some promise [60, 61].
4. Natural language processing of medical documents and literature
Electronic medical records (EMR), the new standard in many hospitals, require complex digital infrastructure. Unification of health data in a formatted manner is a major goal as it should increase the efficiency of hospitals as well as improve patient health outcomes. However, a significant problem is the historical existing physical documentation. Transferring these existing documents into an electronic form is difficult and would be very tedious and expensive if people were hired to manually input such information into an electronic system.
One application of machine learning, which may aid in this problem, is natural language processing (NLP). By scanning these documents rapidly and integrating the resulting images into a database, these systems attempt to extract readable data from free text and incorporates image processing to identify key words and terms. Handwritten physician notes contain information such as patient complaints, the physicians own observations, and patient family history. This clinical information can be annotated. However, poorly worded or inaccurate writing by the physician can make it difficult to accurately assign this information to appropriate categories. Forms and documents that already have structure make for much easier language processing, though there is still the risk of missing data Figure 8.
Figure 8.
Example of a nursing care plan which represents a formatted health document. Most of these plans were filled out by hand and many hospitals have transitioned such forms to electronic records. However, older documents still need to be transferred to digital form. Taken from Wikipedia commons.
Creating a system for improved clinical decision support (CDS) with old patient records is feasible. Any such system is structured to aid in clinical decision making for individual patients based on a database of computerized knowledge. Such a system could be envisioned as two-fold: 1. extracting facts about the patient from their medical record, either through written or typed physician notes or labs or dictation involving audio NLP, 2. Associating possible disease states based on extracted information from previous known cases or through literature search via NLP [62]. Integration of several specialized NLP systems is required for any true and practical implementation of such a CDS system.
Likewise, compilation of the existing scientific research into central repositories is a difficult task. Sometimes physicians may be unaware of a promising new treatment just due to the difficulty of parsing the tidal wave of new papers. Scientific publications have always been widely dispersed across multiple journals and the modern-day information explosion has only exacerbated the issue. When it comes to compiling information such as results from genome-wide association studies (GWAS), the primary method has been a manual curation of the information by certain individuals within the scientific community: “librarians” so to speak.
Recently, a paper published in Nature Communications used machine learning systems to automatically compile GWAS information from open-access publications and extract GWAS associations into a database with the aim of helping curators. Though the results are somewhat inconsistent (60–80% recall and 78–94% precision) it represents one of the many ways NLP is being utilized to aid in medical discovery [63].
4.1 Examples of natural language processing in healthcare research
There are many exciting possibilities where NLP could be used to improve medicine and medical research. We will discuss a few interesting findings with similar approaches but different goals. This is by no means an expansive list but highlights the broad spectrum of possible machine learning applications.
In 2015, a research group published a paper reporting 100% accuracy of predicting onset of psychosis using recorded dialog of clinically high-risk youth. Each youth was interviewed over a period of 2.5 years every 3 months. Based on the transcripts of these interviews, a machine learning algorithm was trained to predict whether a patient would develop psychosis. This was done using what is known as Latent Semantic Analysis to determine coherence of speech using NLP. The sample size for this study was rather small however (n = 34) [64].
Another study used NLP to identify cirrhosis patients and risk-stratify the patients. This study was able to correctly identify cirrhosis patients from electronic health records, ICD-9 code combinations, and radiological scans with a 95.71% sensitivity and 93.88% specificity [65]. This indicates that such a system could correctly identify cirrhosis patients based off existing medical data in most hospitals.
Yet another study used NLP to accurately identify reportable cancer cases for national cancer registries. This method analyzed pathology reports and diagnosis codes to identify patients with cancer patients using supervised machine learning. The accuracy was 0.872 with a precision of 0.843 and sensitivity of 0.848 [66]. The primary goal of this study was to automate the process of reporting cancer patients to the National Program of Cancer Registries in the United States.
These examples of NLP use in healthcare highlight the wide diversity of applications within medicine. Language is the primary means of communicating complex information, doctors’ notes and annotated medical documents hold valuable insights in populations and individual patient health. The irregularity and variance of language and extraction of higher-level information into relevant subcategories makes analysis difficult. Machine learning is showing promising results in performing such complex analyses.
5. Machine learning in genetics for the prediction and understanding of complex diseases
Genetic information and technologies have exploded since 2008, creating difficult challenges in how to handle the exponentially increasing data. Advances in genetic sequencing speed, namely NGS technologies have exponentially increased the speed at which a whole human genome is sequenced, while also dramatically reducing costs. The human genome is a complex physical structure that encodes all the information of human development and characteristics. The genome is highly interconnected and deciphering most of these instructions is still a mystery to us. Variation of genomes between people also increases the complexity of understanding gene interactions.
Many health initiatives have focused on acquiring large sample sizes of human genomes to help identify statistically relevant trends among different populations of humans. However, the 23 chromosomes of the human genome contain around 20,000 genes which have been identified as the primary coding sequences for the proteins necessary in building the biological components of our cells [67]. This number is still a rough estimate and some estimates indicate that there may be as many as 25,000 genes or as few as 19,000 [68, 69]. A large swathe of genetic information that does not code for any proteins is not included in these estimates.
A growing body of literature indicates that certain sections of what has been colloquially called genetic dark matter, or missing heritability, exists [70, 71, 72, 73, 74]. These terms refer to the portions of DNA which have no apparent protein coding function, but may be relevant to the level of gene expression in a person’s genetic code [75, 76]. Levels of gene expression may cause protein overload or deficiency, which can lead to a variety of health problems. Additionally, structural differences in the physical structure of how the DNA is bound into chromosomes and then subsequently unwrapped during both the duplication process and translation and transcription process, can also affect the level of gene expression.
For example, methylation or acetylation of the DNA backbone can make it more difficult (methylation) or easier (acetylation) to unravel the DNA strand during normal cell processes like replication or protein assembly. Evidence of multiple copies of the same gene have also been classified in what is described as copy number variations (CNV) which indicate duplication, triplication, and deletion events of certain areas of the genome in an individual. Understanding this highly interconnected and nonlinear relationship between all the different of the areas of the human genome is difficult.
With machine learning, scientists have begun to find patterns and trends which can be modeled in a more predictable manner. Utilizing the ever-growing amount of genetic data, machine learning has the potential of accurately predicting who is at risk of acquiring certain diseases such as cancers and Alzheimer’s disease. Mental illnesses such as schizophrenia and bipolar disorder have also been known to run in families, indicating a possible genetic link.
5.1 Inherited vs. environmental risk
Disease risk can be broadly categorized into inherited risk and environmental risk. Inherited risk describes a person’s disposition to acquiring complex diseases due to a trait which is genetically passed down from their predecessors. This includes genetic mutations contained within their germline DNA which may predispose them to cancers or other health conditions [77, 78].
Environmental risk describes somatic mutations, or mutations to a person’s DNA due to something they have encountered in their environment. These mutations can still increase a person’s risk of acquiring a disease but they do not affect the germline, and will not be passed on to their progeny and thus will not be inherited [79].
Inherited risk describes mutations that exist in the human germline and which will be passed onto the offspring through normal reproduction. Whereas, somatic mutations may affect organs or a set of cells, germline mutations exist in all the cells of the offspring. Many of these mutations may be passed through paternal lineage and there is some indication that certain individuals may have disease predisposition but which cannot be directly linked to familial history but could still be due to these hidden germline mutations [80, 81, 82].
Several different types of mutations may exist within a human genome. They are broadly categorized as single nucleotide polymorphisms (SNPs), structural variations or copy-number variations (CNVs), and epigenetic variations.
SNPs are a single or point mutation of one base pair in the human genome that occurs in at least 1% of the human population [83, 84]. These mutations are the most common source of genetic variation and can occur both within coding regions and outside of coding regions of the genome. SNPs contribute to vast differences even between relatives and can arise because of both inheritance and development in the womb. Within SNPs there are common and rare variants, with rare variants occurring less than 0.5% within the global sample [84].
Structural variations and specifically CNVs are deletions, insertions, duplications, and inversions of large regions of DNA. These structural differences are usually inherited and a typical human can have anywhere between 2100 and 2500 structural variations [84]. These variations were found to cover more of the human genome than SNPs alone [83].
Epigenetic variation describes variations in the chemical tags attached to DNA or associated structures such as histones, which affects how genes are read and activated. Epigenetics includes DNA methylation and acetylation, histone modifications, and non-coding RNAs which all affect the degree to which a gene may be expressed [85]. As a newer field, it is unclear how much of these epigenetic variations are inherited from generation to generation, and how much is a result of environmental factors [86].
5.2 Prediction of cancers through germline copy number variations
One of the exciting methods we have discovered is the utilization of germline copy number variations in the prediction of different cancers. We have found that it is possible to use machine learning models, specifically gradient boosting machines (GBM), a form of decision trees (DT), to predict whether a person has a particular cancer. The models created were able to predict cancers such as ovarian cancer (OV) and glioblastoma multiforme with an AUC of 0.89 and 0.86 respectively [87], using copy number variation data taken from germline blood samples only. This result indicates that there is a significant inherited portion contributing to cancer risk in many, if not all cancers. Since these CNVs are also taken from germline DNA, the likelihood of continued inheritance to future generations is high.
This method does not look solely at SNPs as many previous methods rely on [88]. Most SNP data specifically looks at mutations within protein coding genes while ignoring the rest of the genome, whereas our method utilizes a whole genome approach by averaging the copy numbers of a person’s entire genome as the basis for predicting cancer. Copy number variation accounts for a large amount of human genetic diversity and is functionally significant though the exact mechanisms are still unclear [77, 83].
These results demonstrate that almost all cancers have a component of predictability in germline CNVs which can be used to predict an individual’s risk to acquiring that cancer Table 2. Experiments were performed on two independent databases: The Cancer Genome Atlas and the UK Biobank. The first database contains about 10,000 individuals and latter contains about 500,000 individuals.
Type of cancer
Cases
Controls
AUC
Breast invasive carcinoma (men and women)
977
8821
0.81
Glioblastoma multiforme
484
9314
0.86
Ovarian serous cystadenocarcinoma
424
4268
0.89
Thymoma
111
9687
0.78
Uveal melanoma
80
9718
0.80
Table 2.
Sampling of performance of GBM models trained on data from the Cancer Genome Atlas.
Future studies may improve on the performance and the models could potentially be used as a tool to assess individual risk for diseases. Since the method can also be easily generalized to other diseases, we anticipate work to continue to encompass other potentially complex diseases which may have inherited components to them.
6. Conclusions
Application of digital technologies such as machine learning in the healthcare field is entering an exciting era. The collision of informatics, biology, engineering, chemistry, and computer science will rapidly accelerate our knowledge of both hereditary and environmental factors contributing to the onset of complex diseases. The potential of utilizing copy number variations in the prediction of cancer diagnosis is exciting. Utilizing machine learning to create an interpretable method of understanding how the genomic landscape interlinks across genes to contribute to inherited cancer risk could potentially improve patient healthcare on an individual level.
Databases such as The Cancer Genome Atlas and UK Biobank are invaluable resources, providing high statistical power to scientific analysis. As other large-scale population data projects near completion in the coming decade, the methods laid on the foundation of The Cancer Genome Atlas and UK Biobank will continue to benefit and improve as sample sizes easily begin to move into the regime of millions of patients. Tracking populations around the world will truly aid in the goal of precision medicine.
Natural language processing will be essential in improving the practicality of translating scientific findings and results of other machine learning methods into a clinical setting. Multiple specialized systems will have to be integrated with each other to effectively extract the wealth of information into a format which can be utilized effectively by physicians and healthcare professionals.
Image analysis is becoming a staple in many diagnostic endeavors and will continue to improve the accuracy of radiological diagnosis. Detection of malignant masses and validation and verification of existing diagnosis has the potential to improve patient outcomes, while reducing errors. As a non-invasive method of looking inside the human body, any improvements in healthcare imaging will reduce the need for risky or ill-informed operations that could lead to other complications such as infections and blood clots.
The examples discussed in this chapter are some of the most promising works in applying machine learning in the healthcare field. Resolving big health data into a usable form will undoubtedly require machine learning techniques to improve. Infrastructure to support such learning techniques is currently not stable or standardized. Bringing such methods from concept to practical clinical use is contingent on both validation of these results and an appropriate infrastructure to support it.
A large variety of devices and storage methods will need to be unified and standardized to benefit from the increased data collection. Information about how human genetic variation can contribute to individual susceptibility allows patients and doctors to make early lifestyle changes in a preventative manner. Likewise, it can inform physicians of which types of prognostics and diagnostics would be the most relevant for a specific patient, saving both time and money, while improving patient outcomes in the long term. Just as AI started with Turing decoding the enigma machine, we are now going to use AI and machine learning to decode the secrets of the human body and genome.
Conflict of interest
The corresponding author is a distant relative of the editor of this book.
Notes/thanks/other declarations
The author would like to thank the University of California, Irvine for support during the writing of this chapter.
\n',keywords:"machine learning, healthcare, big data, medicine, genetics, disease",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72044.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72044.xml",downloadPdfUrl:"/chapter/pdf-download/72044",previewPdfUrl:"/chapter/pdf-preview/72044",totalDownloads:1042,totalViews:0,totalCrossrefCites:5,totalDimensionsCites:6,totalAltmetricsMentions:10,introChapter:null,impactScore:2,impactScorePercentile:75,impactScoreQuartile:4,hasAltmetrics:1,dateSubmitted:"October 22nd 2019",dateReviewed:"March 27th 2020",datePrePublished:null,datePublished:"January 14th 2021",dateFinished:"May 6th 2020",readingETA:"0",abstract:"Machine learning techniques in healthcare use the increasing amount of health data provided by the Internet of Things to improve patient outcomes. These techniques provide promising applications as well as significant challenges. The three main areas machine learning is applied to include medical imaging, natural language processing of medical documents, and genetic information. Many of these areas focus on diagnosis, detection, and prediction. A large infrastructure of medical devices currently generates data but a supporting infrastructure is oftentimes not in place to effectively utilize such data. The many different forms medical information exist in also creates some challenges in data formatting and can increase noise. We examine a brief history of machine learning, some basic knowledge regarding the techniques, and the current state of this technology in healthcare.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72044",risUrl:"/chapter/ris/72044",book:{id:"10150",slug:"smart-manufacturing-when-artificial-intelligence-meets-the-internet-of-things"},signatures:"Christopher Toh and James P. Brody",authors:[{id:"313941",title:"Ph.D. Student",name:"Christopher",middleName:null,surname:"Toh",fullName:"Christopher Toh",slug:"christopher-toh",email:"tohc@uci.edu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313941/images/12963_n.jpg",institution:{name:"University of California, Irvine",institutionURL:null,country:{name:"United States of America"}}},{id:"314005",title:"Dr.",name:"James",middleName:null,surname:"Brody",fullName:"James Brody",slug:"james-brody",email:"jpbrody@uci.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of California, Irvine",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Artificial intelligence and machine learning",level:"1"},{id:"sec_2_2",title:"2.1 Machine learning algorithms",level:"2"},{id:"sec_2_3",title:"2.1.1 Supervised learning",level:"3"},{id:"sec_3_3",title:"2.1.2 Unsupervised learning",level:"3"},{id:"sec_4_3",title:"2.1.3 Hyperparameters",level:"3"},{id:"sec_5_3",title:"2.1.4 Algorithm principles",level:"3"},{id:"sec_7_2",title:"2.2 Assessment of model performance",level:"2"},{id:"sec_7_3",title:"2.2.1 Sensitivity vs. specificity",level:"3"},{id:"sec_8_3",title:"2.2.2 The receiver operator curve and area-under the curve",level:"3"},{id:"sec_9_3",title:"2.2.3 Overfitting",level:"3"},{id:"sec_11_2",title:"2.3 Big data and the health information explosion",level:"2"},{id:"sec_13",title:"3. Machine learning of medical images",level:"1"},{id:"sec_13_2",title:"3.1 Lesion detection and computer automated detection",level:"2"},{id:"sec_15",title:"4. Natural language processing of medical documents and literature",level:"1"},{id:"sec_15_2",title:"4.1 Examples of natural language processing in healthcare research",level:"2"},{id:"sec_17",title:"5. Machine learning in genetics for the prediction and understanding of complex diseases",level:"1"},{id:"sec_17_2",title:"5.1 Inherited vs. environmental risk",level:"2"},{id:"sec_18_2",title:"5.2 Prediction of cancers through germline copy number variations",level:"2"},{id:"sec_20",title:"6. Conclusions",level:"1"},{id:"sec_24",title:"Conflict of interest",level:"1"},{id:"sec_21",title:"Notes/thanks/other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'Copeland J. The turing test. Minds and Machines. 2000;10(4):519-539'},{id:"B2",body:'French RM. The turing test: The first 50 years. Trends in Cognitive Sciences. 2000;4(3):115-122'},{id:"B3",body:'Edwards S. World war II at sea: A global history. The Journal of American History. 2019;106(1):237'},{id:"B4",body:'Turing AM. 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University of California, Irvine, United States of America
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1. Introduction
The increasing esthetic need of patients for orthodontic devices has lead to the development of clear aligner therapy [1, 2]. Traditionally, orthodontists contract with an outside service to provide clear aligner treatments. Outsourcing to a provider has drawbacks for both the patient and the orthodontist. It can take over a month to produce and deliver an aligner set, and the provider requires a substantial service fee, cutting into potential profits.
Advancements in 3D printing technology, Intra-oral scanners, and 3D setup software improve the production of clear aligners. Nowadays, these solutions are widely available in private dental practices, allowing orthodontists in-house aligner production.
In-house 3D printing accelerates aligner turnaround, increases profitability, and improves patient satisfaction while offering complete workflow control.
In this chapter, we will suggest to orthodontists to centralize the production of aligners in the dental office by detailing the different stages of the production flow. Form acquiring extra-oral and intra-oral patient data and exploring necessary hardware and software for this acquisition. Until the production of the aligners, where we will discuss the equipment and materials mandatory for this production. Going through the planning, this section will detail the different software that an orthodontist can use for the 3D setup and the particularities of each of these softwares.
2. Materials and methods
The conventional clear aligner treatment is based on a complete outsourcing workflow, in this flow, the orthodontist will be restrained to check the setup proposal and request changes if he judges it necessary. To refer a case the orthodontist uploads the patient’s data such as photos, X-rays, and digital dental impressions; then, he submits a prescription setup to aligner labs/companies. After a few days, the practitioner receives a setup proposition for review; the orthodontist evaluates the setup made by a technician and asks for some changes if necessary. Generally, there are 2 to 3 revisions with most aligner’s laboratories before achieving a good treatment setup. This interaction between the orthodontist and the technician wastes time. Once the treatment setup has been approved, the orthodontist has to wait for the aligners to be fabricated and shipped to the office. Usually, the whole process takes 2–6 weeks.
In homemade clear aligner workflow, there are three main axes: data acquisition totally made by dental staff, planning of aligner setup, and aligner fabrication; these last two steps can be internalized in the dental office or outsourced to a third party. The outsourcing choice will depend on the time the orthodontist can allocate to planning, the cost/benefit ratio of acquiring software, and hardware and dental staff’s ability to expand functions and competencies Figure 1.
Figure 1.
Different Workflows for in-office aligners.
2.1 Data acquisition
2.1.1 Digital model creation
The maxillary and mandibular digital working models and recording of the patient occlusion can be done directly on the patient by an intraoral scanner or by digitizing the analog impressions and/or plaster models with a desktop scanner or by a cone-beam computed tomography (CBCT).
Extraoral 3D scanners can be used to capture 3D images of both impressions and physical casts to acquire digital models. An optical scanner (OS) is an extra-oral digitization method that uses a white light that is cast on the plaster dental model. Later, the projected pattern is captured using a high-resolution camera, and a 3D image of the model is created. Dental labs often prefer optical digitizers, involving less acquisition time for scan construction [3, 4].
Digital measurements of tooth size, arch width, and Bolton tooth size discrepancy on digital models obtained from plaster dental model scanning and dental impression scanning showed high accuracy and reliability. No statistically significant differences were noticed between direct measurements on the plaster models with a caliper and digital measurements on digital models obtained from plaster dental model scanning and dental impression scanning methods. Digital models can be alternative to plaster models with clinically acceptable accuracy and reliability of tooth size, arch width measurements, and Bolton analysis [5].
Intraoral scanner (IOS) is an alternative to OS for the digitization procedures of plaster dental models [3]. Various intraoral scanners are available in the market, with many different technologies, each with its own limitations, advantages, and costs [6]. The 3D scanning technologies depend on different physical principles and are defined in the subsequent classes [5]:
Laser triangulation 3D scanning technology uses either a laser line or a single laser point to scan across an object.
Structured light 3D scanning technology uses trigonometric triangulation.
Photogrammetry 3D scan scanning technology (photography) reconstructs 3D from 2D images.
Contact-based 3D scanning technology is based on the contact form of 3D data collection and uses a contact probe [7].
Advancements in the CBCT systems have made the digitization of plaster dental models possible [8]. Several CBCT manufacturers have started integrating extra cast digitization tools into their machines to simplify the workflow for data acquisition and surface extraction [3]. CBCT scans are acquired using a volume scan method instead of a surface scan method using a laser or LED source; therefore, CBCT scans are not affected by the angle of irradiation or the shape of the subject around the undercut area proximal contact. CBCT can even be used in cases of crowding without managing raw scanned data [9].
Digital model fabrication using scans of patient impressions obtained with CBCT in a dental office is another alternative method to create a model without an intraoral scanner or a desktop scanner and without directly irradiating the patient. If necessary, digital models and plaster models can be fabricated using a single impression [10].
2.1.2 3D Facial scan
The assessment and analysis of facial soft tissues are essential for orthodontic and maxillofacial diagnosis and treatment planning. In aligner therapy, using a two-dimensional (2D) digital photograph is a basic approach for facial structure assessment. However, this process has been progressively replaced by three-dimensional (3D) imaging. The 3D facial scan enables creating a virtual face that can be integrated with 3D models of the dentition obtained by intra-oral scanners and coupled with 3D radiographic images from CBCT for a 3D orthodontic set-up to achieve virtual patient [11].
There are two classifications of the scanning systems based on the type of equipment of the optical devices, namely stationary systems and portable/handled systems. In stationary systems, the optical devices are fixed on tripods or adjustable frames, while in handled/portable systems, the scanners are movable in real time around the target object [12].
Stationary facial scanning systems based on stereophotogrammetry technology were first introduced in dentistry [13]. Digital stereophotogrammetry captures 3D facial surface data using at least two cameras configured as a stereo pair. This procedure may be: passive or active. In active stereophotogrammetry, structured-light techniques are incorporated for higher resolution [14]. Because of the encumbrance, high cost of this technology, and their operating methods that require frequent calibration, handheld scanning systems using laser or structured-light technology were developed [15].
Laser-based scanners function by projecting an eye-safe class 1 laser beam across a subject’s face. The beam is scattered by the face and collected at a triangulation distance from the laser’s origin. At the same time, Structured-light scanners (SLSs) generate 3D facial models by projecting a full structured light pattern (typically vertical stripes) onto a subject’s face, recording deformations in this pattern produced by the face’s morphology allow 3D face reconstruction [16].
Although most professional handheld scanners are considered acceptable in terms of their scan image quality, they are expensive and often require considerable training time to learn their complex scanning protocols [3, 9, 10]. Alternatively, 3D sensor cameras based on structured-light technology have been developed for smartphone and tablet devices [15]. Increasing interest is due to mobile devices’ high portability, user-friendliness, cost-effectiveness, and popularity [17, 18, 19]. The advantages of smartphone face digitization include reducing time for scanning, image processing, technical learning [20, 21], and their high portability [22].
Motion artifacts were considered the primary source of error in the results of portable face-scanning systems [23, 24, 25], cautioning that the influence of involuntary facial movements has a more significant impact on mobile face-scan devices than stationary ones [11]. Prolonged scanning time and unstable movements of the scanners may magnify the motion artifacts caused by involuntary facial movements [25]. Therefore, using scanners that conduct a single and quick scan is recommended, mainly when the face scans are performed on children or people with special needs who struggle to stay immobile for a prolonged time [11, 25, 26].
2.1.2.1 3D dentofacial integration
The 3D dentofacial image integration is performed by matching the dental scans to the facial scans. Alignment of the two scans (facial scan and dental scan) can use teeth image only (TO), perioral area without marker (PN), or perioral area with markers (PM) [22].
For the 3D dentofacial integration using teeth images only, the teeth area visible on the facial scan images is used as a reference to match the facial scan with the intraoral scan Figure 2 [27, 28].
Figure 2.
Alignment of the two scans (facial scan and dental scan) using teeth image only (TO).
The intraoral scan of the teeth area associated with the scan of perioral structures was proposed to enhance the accuracy of the dentofacial integration [29] Figure 3. This procedure aims to provide larger areas that can be used as a reference to coordinate the intraoral scan of the teeth with the 3D scan of the face. The effect of the perioral scan method on image matching depends on the use of artificial markers during the perioral scanning [22]. The absence of clear marks on the skin causes inaccuracy of the scan data obtained when capturing large areas of the perioral structures without the skin marker attachment by the intraoral scanner.
Figure 3.
Alignment of the two scans (facial scan and dental scan) using perioral area without marker (PN) The participant was scanned using Bellus 3D by rotating the head to the right and the left of the camera, following the manufacturer’s instructions while maintaining the head at the camera’s center. The scanning mode was set in high-definition (HD mode) in the scanning software. The intraoral and perioral anatomical structures were acquired using an intraoral optical scanner mediti500. The perioral structures, including the upper lip, philtrum, and nose, were obtained with the participant’s anterior teeth in a broad smile position. a: The first step is matching perioral scan to intraoral scan; fixed mesh is intraoral scan. b: The second step is matching the 3D facial scan with the perioral scan previously aligned on the intraoral scan; the fixed mesh in this step is the perioral scan.
Artificial markers provide distinct references for similar adjacent areas so that they could help the image stitching process. Perioral scan with artificial skin markers significantly improved the accuracy of integration of dental model to the facial scan Figure 4 [22].
Figure 4.
The two scans (facial scan and dental scan) are aligned using perioral area with markers (PM). A: The first step is matching perioral scan to intraoral scan; fixed mesh is intraoral scan. B: The second step is matching the 3D facial scan with the perioral scan previously aligned on the intraoral scan; fixed mesh in this step is perioral scan. Artificial skin markers provide distinct references for the image stitching process.
2.1.3 3D X-ray: Cone-beam CT
Major planning solutions for aligners consider only the crown position, not the root shape. Complete tooth architecture information, including crown and root anatomies, would improve treatment planning and provide more predictable results [30].
2.1.3.1 Procedure
Dicom file is imported into 3D setup software; the orthodontist performs segmentation to have a 3D reconstruction of root morphology, then he stitches 3D segmented teeth to STL IOS model. Afterward, the orthodontist can adapt the position of the virtual tooth to segmented roots to have a correct pivot. Integrating 3D data from an optical scanner with volumetric data from CBCT imaging provides an optimal spatial reference for the most accurate hard and soft tissues models. Figures 5 and 6.
Figure 5.
Aligning 3D segmented Teeth (Roots & Crowns) to IOS Scanned teeth using teeth as references.
Figure 6.
Aligning virtual teeth of 3D setup software according to segmented roots (CBCT).
2.2 Digital treatment planning
Selecting software is the main concern for most clinicians to get started with homemade clear aligners. All 3D setup ortho planning software have typical workflow Figure 7. The software’s options have comparable abilities at the core; however, some specific features add value and are determining when choosing a software. Table 1 summarizes the different software available on the market with their respective options.
Figure 7.
Typical workflow for 3D ortho setup software.
3shape
Sursmile
C+ model
Ulab
Ortup
BSB Ortho
ArchForm
SoftSmile
Grid overlay features
✓
✓
✓
✓
✓
✓
✓
✓
Automated Segmentation
✓
✓
✓
✓
✓
✓
✓
✓
✓
Individual or group movement of teeth
✓
✓
✓
✓
✓
✓
✓
✓
✓
Customize attachement size/dimension
✓
✓
✓
✓
✓
✓
✓
✓
Auto place attachment
✓
✓
✓
✓
✓
IPR adjustment per contact
✓
✓
✓
✓
✓
✓
✓
✓
Staging IPR steps
✓
✓
✓
✓
✓
Same day starts
✓
✓
✓
✓
✓
Automated set-up
✓
✓
✓
✓
✓
✓
Print horizontal
✓
✓
✓
✓
✓
✓
✓
✓
✓
Print vertical: add platform
✓
✓
✓
✓
✓
Printing hollow
✓
✓
✓
✓
✓
Labels Models
✓
✓
✓
✓
✓
✓
✓
✓
✓
Aligners setps on models
✓
✓
✓
✓
✓
Atomated aligner trimming for milling machine
✓
✓
Predectibility and gradiant difficulty for tooth movement
✓
License fee
✓
✓
✓
✓
✓
✓
✓
Fee per case/aligner exported
✓
✓
✓
Directly print Aligner
✓
✓
Pontic for extraction Cases
✓
✓
Virtual root
✓
✓
✓
✓
Table 1.
Different software available on the market with their respective options.
2.2.1 Automatic segmentation
Almost all programs offer an automatic segmentation feature. Artificial intelligence (AI) algorithm finds the gingival border of each tooth. Using AI, the software will automatically segment and identify the teeth. Next, they will label the teeth and then automatically create a long axis, center groove line. If necessary, the software can manually adjust borders with an intuitive brush-editing feature, edit tooth labels, correct grooves, and adapt the long axis if needed [31, 32].
2.2.2 Realtime simulation
3D ortho setup software authorizes real-time simulation with features as intuitive alignment, enabling easily drag teeth to where they need to be, occlusal contact collision calculation, and IPR options. Also, 3D ortho setup software allows aligning the teeth to a customizable arch shape by adjusting the arch shape using the control points placed around it [33].
However, not all programs allow skeletal movements, evaluation of multiple treatment strategies, and creating treatment simulations for surgical, restorative, and extraction cases [34]. Plus, features relative to model capabilities as Bolton analysis on every model, automated measurements of tooth width, arch width are not available in all software.
The SoftSmile, Blueskyplan orthodontic, Deltaface, and Orth’up aligner software [31, 32, 33, 34, 35, 36] create a 3D model of the orthodontic treatment plan, including a representation of teeth roots and movement of the lower jaw during the treatment. It creates optimized teeth movement and suggests, along with the knowledge and skill from the orthodontist, the exact number of aligners needed for reaching better results.
2.2.3 Advanced staging and sequencing
3D setup softwares make a staging proposal; the user feels the difference in the possibility of customizing this staging. BSB ortho, uLab, et ArchForm enable the orthodontist to select the teeth to move first, achieving sequential distalization and establishing the order of teeth movements [32, 33, 34, 35].
2.2.4 Attachments
Adding an attachment is a standard option in 3D setup software. Some softwares stand out by features such as automatic attachment placement depending on the tooth movement or customized attachment with adjustable attachment size and gingival tilt to control tooth movement [35, 36, 37].
2.2.5 Ready to print models
From finishing the treatment plan to starting a print, much valuable time is lost on preparing printable.STL. All softweares allow STL export, but some make the entire manufacturing process smooth, intuitive, and straightforward.
Blueskyplan ortho, Archform and ULab automatically prepare models for 3D printing: in few clicks, all models are made hollow, and a bar for vertical printing without support is attached to them [35, 36, 37]. Usually technicians spend 5–7 minutes on the preparation of each model, but with BlueSkyPlan Ortho 2 minutes are spent on preparing the whole case’s models. Features like hollowing models and vertical printing with optimized tilt make the virtual setup process smooth, quick and convenient, saving resin and printing time [35].
Labelling models is a standard feature that enables adding letters and numbers on models to identify patients and orthodontists. Nevertheless, special labelling such as auto labelling imprints onto the aligner is specific to only some software like BSB ortho, Archform, and Ulab [35, 36, 37].
2.2.6 Automatic pontics for concealing gaps and missing teeth
Developed especially for extractions cases, this functionality is not available in all software. On Archform, and ORTH’UP softweare [33, 37], teeth can be extracted at any stage during treatment planning. The two software allow clinicians to place a pontic that will change dimension as the space is closed. The pontic can have the same form as the extracted tooth, a mirror of the tooth on the other side, or a tooth selected from a library [37]. With SureSmile, either gaps are opened for an implant or closed after an extraction; once a space is bigger than 3 mm, a virtual tooth is added to fill the gap [34]. Efficient and fast, this functionality allows significant time-saving in the preparation of cases for the dental assistant.; avoiding manual waxing on printed models before thermoforming aligners [33].
2.2.7 Variable trim line
With BSB ortho, doctors can freely choose the trim line design; individualized positioning bases are added to the aligner to be trimmed in a high-precision automated laser cutting machine [35]. The Aligner Trim curve will be generated automatically based on the parameters “Curve Shape” and “Trim Margin” in Preferences. Both parameters can be adjusted as well and regenerate directly on the orthodontics panel. The export of the curve will be available in the last step for the automatic trimming of the aligners in the milling machines [35]. ORTH’UP software offers the possibility of calculating the aligner boundary at each step of the treatment plan and converts it into a 3D marking on the printed model. This visual reference makes cutting the aligners by the dental assistant faster and much more precise [33].
2.3 3D Printing
The dental sector has been undergoing radical change for many years, thanks to the digital dentistry movement. Additive manufacturing, in particular, has enabled the dental industry to expand its use of digital technologies. Indeed, the dental sector is a promising market for 3D printing technology because it responds to the issue of customized items.
3D printing is now easily approachable for orthodontists; 3d printing for orthodontics reduces production time and costs, and its potential is still growing [38].
2.3.1 Fused deposition modeling (FDM) 3D printing
Fused Deposition Modeling (FDM) 3D printing consists of creating several layers by injecting a molten plastic filament through a heated extruder. Any material that can be injected through a heated nozzle at melting temperature is printable by this technology. It comes in a long filament with a 1.75 to 3 mm diameter wound in a 500 g or 1 kg coil. Polylactic acid (PLA), Acrylonitrile butadiene styrene (ABS), and GreenTech pro are the most suitable materials for orthodontic models. Their prices vary from 20 to 40 euros [39].
PLA is a fully biodegradable polymer by industrial composting. It is obtained from the fermentation of starch, beet, corn, or sugar cane. It has the advantage of not giving off toxic fumes during printing. However, its glass transition temperature is around 60°, which limits its use under thermal stress, which goes against the thermoforming of aligners [39, 40]. PLA is generally used in 3D printing due to its very affordable price also in dental 3D printing to make dental models. New reinforced forms are proposed to endure mechanical and thermal stresses. (Pla Ultra, PLA-X3,) [40, 41].
Acrylonitrile butadiene styrene (ABS) is a thermoplastic polymer with excellent mechanical and thermal resistance. It is very affordable and is easily recycled by steaming [42, 43].
GreenTech pro is a 100% biodegradable biopolymer (DIN EN ISO 14855), made from organic, CO2 neutral, and environmentally friendly materials. The FDA has approved it for food contact. It has a mechanical and thermal superior resistance to ABS and PLA, ideal for dental models subject to thermoforming constraints [44].
2.3.2 Stereolithography 3D printing
Stereolithography 3D Printing (SLA) is the most widely used technology in dentistry, both for its precision and well-finished surface. For the same layer thickness, the surface roughness is far well finished compared to FDM. Stereolithography (SLA) is an additive manufacturing process that refers to the Vat Photopolymerization family. In SLA, an object is formed by selectively curing a polymer resin layer-by-layer using an ultraviolet (UV) laser beam [45, 46].
UV light can be a simple micrometers laser beam that will sweep the entire layer, point by point, just like a colored pencil that colors a 2D drawing to follow the same way on the next layer [45]. UV light projection can also be a light projection of an entire layer by a DLP projector (Direct Light Processing), resulting in a single-shot polymerization of the entire layer. Compared to SLA, the DLP is definitely faster [45].
Among the leading manufacturers of 3D SLA printers, 3D Systems, is at the origin of this technology, but also more recent players like Asiga, which was the first to have launched the Direct Light Processing (DLP) 3D printers in 2011, and Formlabs, which initiated the introduction of in-office 3D printers to the dental practice through its FORM2 printer allowing 3D printing dental materials.
This technology uses 385 nm or 405 nm photopolymerizable resins depending on the wavelength of projected light. There are many resins dedicated to dental models which have the advantage over other resins of being very fast in printing and having a color that helps thermoforming control and good mechanical and thermal resistance.
2.3.2.1 Dental model resin
All resin manufacturers began to produce dedicated dental resins for both prosthodontic and orthodontic models. Compared to standard resins, those resins have faster print speed, are very precise, and have a significantly lower degree of shrinkage. Dental models resins have a beige color [47].
2.3.2.2 Dental long term (LT) ® clear resin
It is a class IIa long-term biocompatible resin for printing rigid splints, durable orthodontic appliances, and night guards. According to some preliminary studies, this resin may be suitable for clear aligner direct 3D printing because it has good geometric precision and comparable mechanical properties to the thermoformed aligners [48, 49].
2.3.2.3 Tera Harz TC-85
Graphy, a South Korean-based company of 3D printable photopolymer resins, has revealed a dental 3D printing material mark, Tera Harz, intending to overcome the constraints posed by other 3D printable resins used within the dental field.
Graphy’s Tera Harz has obtained CE, FDA, and KFDA medical device certification and is available in clear (TC-85DAC) or white (TC-85DAW). The clear Tera Harz resin is fully transparent and has high durability agreed with orthodontic treatment device purposes. In comparison, the white Tera Harz material features esthetics alongside durability Figure 8.
Figure 8.
Directly printed aligners with Tera Harz TC-85 resin (TC-85DAC) put, after post-treatment side by side with thermoformed aligner (Biolon 0,75 mm).
2.3.2.4 Post treatment
Objects produced with 3D printing technologies usually need some degree of post-production treatment. This crucial step of the 3D printing workflow is known as post-processing. First, 3d printed models must be washed in isopropyl alcohol (IPA) or tripropylene glycol monomethyl ether (TPM). For optimal cleaning, users have to shake parts around in the solvent as well as soaked. Habitually, cleaning 3D Printed models requires two washes in IPA or TPM to be fully clean.
When an SLA part finishes printing, the polymerization reaction may not yet be completed. Wich means that parts have not reached their final material properties and may not function as expected, particularly tough parts under strain. Exposing the printed objects to light and heat, called post-curing, will help solidify its materials properties. A UV box post-treatment is usually required to achieve the light-curing process and maximize material strength.
Post-curing is not mandatory for standard resins. Other resin types require post-curing to achieve their optical-mechanical properties. Each material should be submitted to the curing process for a specific amount of time. Printed models should be cleaned and cured before removing supports [46].
2.4 Thermoforming aligners
The first aligners developed by Align Technology corresponded to a single-layer rigid polyurethane produced from methylene diphenyl diisocyanate and 1,6-hexanediol. To enhance its mechanical proprieties and transparency Smart Track (Align Technology, 2012) developed a new thermoplastic polyurethane [50, 51]. The expansion in demand for clear aligners has commanded the development of additional thermoplastic materials for clear aligners by other entreprises, such as e.g. Invisalign, Duran, Biolon, Zendura, Erkodur, ClearCorrect, Erkoflex 95, Erkoloc pro, etc. [52, 53] Table 2 summarizes the different sheets currently on the market [54].
The manhood of current aligner companies uses transformed polyethylene terephthalate glycol (PETG), although polypropylene, polycarbonate, thermoplastic polyurethanes, copolyester, and many other materials are also used [50].
The mechanical characteristics of dental polymers exhibit a myriad of influence factors, such as intrinsic factors (molecular and crystal structures, etc.) and extrinsic factors (temperature, humidity, etc.) [55, 56]. The used polymers are either amorphous or semi-crystalline. Low crystallinity of polymers typically means high flexibility, high elasticity, and adaptability to the tooth shape, but on the other side, they present low tensile strength, low chemical resistance, and stability [56]. From a clinical perspective, polymers with high flexibility and elasticity are more convenient for patients to insert or remove the aligners. Furthermore, they adjust better to the complexity of the tooth anatomy, attach perfectly to any surface. Correlated to aligners of rigid materials, they also guarantee continuity of force expression during the orthodontic treatment [56].
2.4.1 Thermoforming machines
Thermoforming consists of hot shaping thermoplastic products made of polymers. There are two types of thermoforming machines:
Vacuum forming machines that operate on the principle of air depression. A draft takes place below the model to be thermoformed, thereby ensuring the plastic material’s suction above. Example: Essix Machine®, Tray Vac®, Econo Vac®, Erkopress 240® [57].
Pressure forming machines that generate pressurized air above the thermoplastic material to press it against the model. Steel granules partially coat the model limiting thermoforming to uncovered areas. Example: Ministar®, Erkoform® or Drufosmartscan® [57] Figure 9.
Figure 9.
Pressure forming machines for aligner’s fabrication.
Vacuum forming machines are not recommended for making aligners because they are not accurate enough. The aligner must have a tight fit on the models to transfer that fit over the teeth and have the proper amount of force. For this purpose, pressure-forming machines are more adapted. These machines are usually already present in the dental office for making retainers, night guards, etc.
The selection of a forming machine will be made according to the compatibility of the machine to different brands of trays, the space allocated to thermoforming in the dental office, the Drufosmart® for example, takes up a little less space than the others because of its vertical forming design, or according to features that will facilitate and automate the task of dental assistants, such as the barcode reader where the materials setting are just scanned, or the possibility of thermoforming several models at the same time for mass production.
2.5 Tray trimming and polishing
After thermoforming, the aligner is first cut on the 3D printed model with large chisels; then, it is delicately removed to avoid permanent deformation on the aligner. The cutout is finished with curved scissors. Polishing the edges is done with polishers to avoid having sharp edges. Solutions for automated trimming exist on the market like Inlase for dental practices with an expanded production volume of aligners [58]. There are solutions for automated trimming on the market like iNLASE®, which is a laser trimming machine that automatically cuts thermoformed aligners in less than 15 seconds, without the need for manual cutting or polishing Figure 10 [58].
Figure 10.
In-office trimming of aligners.
2.5.1 Scalloped VS continuous curve
According to Cowley et al. [59], there are three designs for aligners at the gingival margin:
A scalloped gingival margin design, along the gingival zenith, which is used by Invisalign and Orthocaps.
A straight line gingival margin along the gingival zenith.
A straight-line gingival margin above the gingival zenith (which is used by CA Clear Aligner) [59].
The difference between the techniques was remarkable. The straight cut 2 mm from the margins was about twice as retentive as the scalloped cut for clear aligners without engagers. For clear aligners with attachments, the straight cut 2 mm from the margins was over four times as retentive as the scalloped cut.
Cutting the aligners differently had more of an impact than supplementing or excluding attachments. Aligners are more comfortable with this technique because the aligners impinging on the unattached marginal gingiva is less risk. The edge of the aligner is covered further under patients’ lips during everyday use; this should also slightly increase the discreetness of the aligners.
2.6 Packaging and delivery
Packaging and labelling is a step that is often overlooked in aligner fabrication. Standards bags with a zip-lock function can be easily found on the market and handled for aligner packaging. Practitioners can easily utilize labels and print office logos and patient information. A bag or a box can be used to deliver the aligners to the patient; custom printed plastic bags are preferable to boxes. Besides being more cost-effective, custom printed bags take up less space and are easier to stock and deliver to the patient, particularly when only a few stages are required. From a branding perspective, practices with in-house aligner production should package the aligners in a way that promotes their office Figure 11.
Figure 11.
Homemade packaging for aligners.
2.7 Delegation
Delegation is a fundamental concept in management. It allows the practitioner to “optimize his diploma” by performing only acts or tasks which fall solely and specifically within his competence. On the other hand, it helps develop team motivation. Delegating tasks relating to new technologies such as 3D printing or digital impression helps to motivate and, above all, enhance dental assistant work.
For homemade aligners, 90% of the tasks can be delegated to a dental assistant. 10% of the remaining tasks concern planning of 3D setup, some steps of which can also be delegated. When outsourcing 3D setup, the whole production chain is delegated. Table 3 shows the distribution of tasks relating to the homemade aligner.
Orthodontist
Dental assistant
Clinical work
Digital models
Intra-oral scan
Closing model’s holes
Exporting for set-up
Software/outsourcing
2D X-Ray- CBCT
Taking 2D X-Ray/ CBCT
Facial
2D photos 3DFacial scan
Aligner’s Initial insertion
Seats check
Laboratory work
Planning Software
Aligning teeth
Choosing attachments
Staging
Loading models in software
Marking teeth
Segmenting teeth
Pre-aligning teeth
Labeling models
Adding Platform
Exporting for 3D (hollowing models)
3D printing
Nesting models in 3D printer
Software
Post processing
Removing models
Washing & drying models
UV Curing models
Taking off platform /supports
Aligner fabrication
Thermoforming
Cutting/trimming
Polishing
Packaging
Table 3.
The distribution of tasks relating to the homemade aligner.
The dental assistant must do all patient records. Indeed intraoral scanning, taking 2D or 3D X-rays, and face scanning all these tasks can be delegated to a well-trained dental assistant.
The dental assistant will import /export various STL/OBJ files either to prepare the 3D setup or to print the various stages. The dental assistant will also process data such as tooth segmentation, labelling, and nesting models on 3D printer software. The interoperability and intuitiveness of the software will allow the dental assistant to switch from one software to another seamlessly.
All tasks relating to 3D printing are delegable: removing models from building platforms, washing, drying, curing models, and removing supports. When choosing a 3D printer, the practitioner should consider user-friendly and intuitive 3D printing software that exports the models with the bases set at the correct angle. Likewise, selecting a 3D printer with features like calculating the amount of resin or filament needed for 3D printing to not run out of materials is crucial for overnight 3D printing. Samely when purchasing post-treatment hardware, the practitioner must choose automated systems for washing, drying, and curing models to make the task as efficient as possible for the assistant.
Aligner fabrication is a fully delegable task; the dental assistant must do the entire process, thermoforming, cutting, polishing, and packaging. Thus, the dental assistant performs the initial insertion of the appliance to check its fitting.
3. Results
Invisalign is the most common clear aligner option that is outsourced. The cost for Invisalign treatment is 575 $ for five aligners, 1199$ for 14 aligners cases, and 1779$ for full cases. For ClearCorrect, the price for five aligners is 395 $, 935$ for 14 aligners cases, and 1495$ for unlimited cases.
When aligners are homemade, the cost for five aligners treatment turns around 70$. This includes printing, materials, assistant time to fabricate the aligner, and software fee. The cost per printed model is for resin models 1,75 $, and it depends on the brand of the resin and the use or not of supports while 3D printing. The cost per clear aligner sheet is 1,5$ (biolon 0,75 mm), and it also depends on the brand of the aligners sheet. In the USA dental assistant’s average wage per hour is $ 25; for aligner fabrication, a dental assistant takes 5 minutes to make each clear aligner, so the cost per aligner for assistant time is roughly 2$. The total fabrication cost per aligner for homemade aligners is 5,25$. For an in-house clear aligner software, the fee per case is 20 $ for two arches (Bluesky plan ORTHO) and 10$ if only one arch is processed. If the orthodontist wants to outsource the planning, the cost for outsourcing planning is $ 200 (LabPronto). Table 4 recapitulates the different costs according to the treatment options and the number of aligners.
Comparison cost fee for different aligners systems.
5 $ fabrication cost per aligner and 20 $ software cost per case (2jaws) (10$ one jaw). Cost per aligner include materials cost/ printing cost and assistant’s time to fabricate the aligner.
200$ for outsourcing planning (Labpronto).
4. Discussion
Orthodontic practices that integrate in-house aligners solution into their operation gain full control over the workflow eliminate outside lab fees, and achieve faster production turnaround time. Internalizing aligners manufacturing in the dental office reduces by at least half of the cost compared to commercial aligners suppliers Table 4.
Being able to reduce aligner fees for patients will increase profit line and case acceptance. Nowadays, direct to consumers companies propose clear aligners with competitive cost compared to conventional aligner treatment. Thus the do-it-yourself (DIY) aligners companies are trying to eliminate the orthodontist from the equation. With the homemade aligners the orthodontist can be competitive even with such companies.
In-office aligner’s production allows complete management for the entire aligner-making process. Compared to a custom commercial aligners laboratory, this flow enables complete control over the treatment plan because planning is done by the orthodontist and gives particular options like having additional aligners/refinement or producing several aligners for the same step in different thicknesses for specific case’s need.
Orthodontists have also control of the 3D printing process: by controlling materials, resolution, printing direction, models Hollowing, etc.. and managing aligner sheets materials in terms of composition, thickness, toxicity (Bisphenol A (BPA) free) [60], and the trim line, also being able to customize this factors for each specific clinic case. All these aspects have a significant impact on the efficiency of clear aligner therapy.
In-house aligner production authorizes faster processes for patients; Aligner production can begin as soon as the patient is ready to undergo oral scans. Practices can provide same-day or next-day starts service depending on the patient queue. In a same-day appointment, an orthodontist can take oral scans, plan out treatment, and print and form the first aligner stages before the patient leaves the office or within a few hours of the appointment. The expedited service provides optimal customer service and an immediate customer lock-in advantage.
4.1 Digital model creation
Digital models offer more advantages such as instant accessibility of 3D information without the need for the retrieval of plaster models from a storage area, reduced need for large areas for plaster model storing, and less time-consuming analysis [61]. With 3D digital models, clinicians can evaluate dental models in three-dimensional aspects and perform dental analysis in more detail. Interrelation between maxillary and mandibular arches can be better observed in occlusion on different scenes in 3D software [62]. Digital models also provide virtual treatment and virtual setup [63]. 3D models can be processed to analyze specific teeth and to estimate the axis or position of individual teeth, which provides a three-dimensional prediction of tooth movement by superimposing dental changes on stable reference structures [5].
Desktop Optical Scanning is a simple, fast, and straightforward procedure; models do not require a second scan due to the scan’s lack of data or non-completion. Likewise, the OS procedure is an entirely delegable task. However, despite all the advantages, it is very cost-intensive and therefore unaffordable for many dental offices and labs, and for impression scanning procedures, the record of the patient’s occlusion cannot be obtained [3].
Intraoral scanners introduce innovations in orthodontics such as monitoring dental movement through digital model superimposition aligners [64, 65], further customization of orthodontic appliances such as removable retainers [65], and last but not least, more accurate diagnosis, treatment planning and even simulation of possible orthodontic movement on appropriate software [66, 67]. Furthermore, scanning requires more chairside time, but it was found less unpleasant than the standard procedure of impression taking [68]; evidence exists that patients when asked which type of impression satisfy them more, choose digital due to patient-centered outcomes [69].
A systematic review [70, 71] of the accuracy of intraoral scans reported that inter-and intra-arch measurements from intraoral scans were more reliable and accurate in comparison to those from conventional impressions. Another systematic review in prosthodontics [72] reported that dental restorations fabricated using digital impressions exhibited a similar marginal fit to those fabricated using conventional impressions [73].
Many factors affect the accuracy of the IOS, such as [74, 75]:
Scanner: capacity to register details and its accuracy.
OperatorUser: scanning fundamentals and path’s scanning.
Scanning area: the dimension of the scanning area, arch length, and surface irregularities.
Intraoral environmental factors: temperature, relative humidity, illumination, shiny, reflective, or transparent objects [7]. Solabrietta et al. denoted that the differences in accuracy between the scanners are rudimentary, and the characteristics that make everyday work easier and more pleasant for the doctor and the patient seem to be much more relevant [61].
After a conventional alginate impression, a median of 22 minutes is required for plaster modeling, including pouring and trimming. In Park JY, study [9], the digital models were obtained within 5 minutes after a rubber impression, with 14 seconds for the CBCT scan of the impression, 1 minute for CBCT file export, and 2 minutes for generating an STL file for each arch. In terms of efficiency, digital modeling using CBCT seems to be clinically feasible and is correlated to reduced laboratory time. No significant differences were found in most measurements between the cast scan models and CBCT digital models. CBCT may be suitable for use in clinical practice because of its advantages, including a reduced working time for digital model rendering [9]. For a dental professional who previously has a CBCT or an IOS device, the acquisition of another digitization system might seem redundant [3].
However, the 3D ortho setup is done on maxillary and mandibular 3D models in occlusion. Using a CBCT to digitalize dental arches is undoubtedly possible. However, the registration of the occlusion, which is indexing one model in relation to the other with this method, is not as intuitive as with an OS or IOS and will require additional CBCT scans of the models in occlusion and the passage through a third-party software to align, relate and index the models before importing them into the 3D setup software.
For Emara A, OS is the best choice for dental models’ digitization. The CBCT, however, proved to be a highly precise option. Even if the tested IOS showed the lowest results in terms of accuracy, it is still a valid affordable option for model digitization, with results falling within the “clinically acceptable” range [3].
4.2 Facial scan
Facial scanner using a mobile device 3D sensor camera has been captivating much interest in recent years because it is highly portable and cost-effective and because of the popularity of mobile devices [14]. Smartphone- and tablet-compatible 3D facial scanners have been described to be a valuable tool for clinical use in prosthodontic treatment [12, 15, 16, 17, 18]. However, the digital facial impression accuracy obtained with mobile device–compatible face scanners has not been investigated [15].
No significant difference was found between stationary and portable face-scanning systems concerning the accuracy of the resultant digital face models. Within the comparison of scanning methods, stereophotogrammetry, laser, and structured-light systems showed similar levels of accuracy in generating a digital face model [11].
The accuracy of mobile device–compatible face scanners in the 3D facial acquisition was not comparable to that of professional optical scanning systems, but it was still within the clinically acceptable range of <1.5 mm in dimensional deviation [15].
Amornvit et al. [76] and Liu et al. [77] reported that mobile device–compatible face scanners are comparable to professional 3D facial scanners when scanning simple and flat areas of the face such as the forehead cheeks, and chin. However, scanning accuracy was relatively low when mobile device–compatible face scanners were used to capture complex facial regions, such as the external ears, eyelids, nostrils, and teeth [76, 77, 78, 79]. Higher inaccuracy was found in the facial areas with defects, depending on the depth of the defect [15, 20]. The teeth scan quality for the smartphone 3D face scan could be lower than that of the stereophotogrammetry because of the high sensibility to the depth of the smartphone facial scanner [16, 22].
The accuracy of the image integration using teeth images only principally relies on the spatial accuracy and the resolution of the captured anterior teeth image in the digital facial scan [28]. When only the teeth region was used for image matching between the facial scan and intraoral scan images, the alignement could be predisposed to error because of the image deformations of the 3D facial model at the mouth area due to the difficulties in scanning the complex structures of the teeth and the gingiva [22, 28].
The accuracy of virtual dentofacial combinations was mainly reliant on perioral scans and artificial skin markers. The most trivial midline deviation and frontal plan canting were found when the perioral image with artificial markers was used. In contrast, the highest divergences were found when the perioral image obtained without markers was employed for image alignment. Although stereophotogrammetry face scan generally showed higher accuracy of virtual dentofacial integration than the smartphone 3D depth camera face scan, the difference between the devices was not significant when the perioral scans were used as references for image matching.
4.3 Setup planning
Unique features make some software high valuable, when choosing software for homemade aligners, orthodontists should look for a program that includes the functionality of matching CBCT data to IOS data and the possibility of positioning the virtual roots of the 3D setup software according to 3D segmented teeth from CBCT. Accurate superimposition of the intraoral scan over the CBCT data would allow the orthodontist to clearly view a dimensionally true representation of a tooth and its root relative to the alveolar ridge [80, 81]. While the conventional virtual setup focuses on moving the crowns, the 3D digital model includes root positions, thus enabling a better outcome [82, 83, 84].
BSB ORTHO offers advanced options such as integration of CBCT and facial scan data, the superposition of these data with the 3D models is seamless with BSB ORTHO software, also import and export high definition models to have as little decimation as possible and achieve a good fitting of the aligner [35].
Archform, uLab, and 3Shape software create the same-day functionality without spending time creating a complete treatment set-up. This adds value for the clinician offering super-speed turnarounds and bringing instant orthodontics into their practices [84].
Carestream’s Model+ software is a relative newcomer to the aligner software space; Carestream’s Model+ software has a unique feature that only is within their software. Model+ allows the clinician to assess individual tooth movements and grade both case complexity and predictability of individual tooth movements [84].
ArchForm can be used across multiple computers and keep patient data in synchronization. For example, the orthodontist can start a design on the office computer and continue it on his laptop at home. Plus, the software keeps patients on track, turning around refinements in one day by instant adjusting treatments mid-course for faster treatment and more precise results [37].
ArchForm and ULab’s AI-assisted software includes one-touch bracket removal features that make finishing bracket cases in aligners or preparing finishing retainers in advance easier by allowing easy removal of the brackets post-scanning [85].
Direct 3D printing of aligners is more innovative and is gradually gaining market share, especially with the emergence of more suitable resins. It is a breakthrough. Deltaface & BSB Ortho are the only two software on the market that offer this functionality; the rigidity of the aligner is set on that software by locally adjusting the thickness of the aligners. This technic offers many advantages, notably: better precision, saving of time by eliminating the steps of thermoforming, cutting, and polishing; it also allows a saving of resin by removing the need to print the models, which has an ecological virtue [31, 35].
Many software options require monthly subscription fees, pay-per-case export fees, or pay-by-aligner pricing, and it is crucial to select cost-effective and functional software for the office.
4.4 3D printing
FDM printer extrudes a resin that has been heated just beyond its melting point, placing it layer by layer. The heated material hardens immediately after being extruded, thus minimizing inaccuracies. Of the available materials, the most common are polylactic acid and acrylonitrile butadiene styrene (ABS). These often come on spools that can easily be replaced as needed. FDM 3D printing has the advantage of printing at a low cost and not needing post-processing, but it is relatively slow and less well finished than stereolithography. However, it offers relatively sufficient precision for orthodontic models because it easily makes dental models print with 100 to 50 microns accuracy with semi-professional 3D printers like the Ultimaker S5 and Raise3D E2. It is possible to recycle old ortho models through filament extrusion machines (for example: 3DEVO) to achieve almost zero production cost and ecological production [86].
Nanometric particles are emitted during ABS 3D printing process and are harmful if inhaled. To avoid the harmful inhalation of these particles, practitioners who want to integrate this technology in their practice area should use a fully enclosed 3D printing room equipped with a fume extractor-ultrafine particle emissions from desktop 3D printers [87]. Adding adherent agents on the printing bed is strongly recommended to limit the warping (Detachment of the part from the plate during printing) of the ABS [86].
Generally, there is no post-processing for FDM 3D printed dental models as they are generally horizontally printed and do not need any supports or printing platform. Despite being slow, this technology requires the minimum intervention from the operator because after detaching the model from the printing bed, models are prompt for thermoforming process.
In the aligner-manufacturing context, biocompatibility resin is not mandatory except in direct 3D printing aligners that will emerge soon. However, according to other authors, the Dental LT could be subject to an overall thickness inaccuracy compared to the designed file, leading to undesired movements [88]. In addition, 3D printing orientation and post-processing conditions; (exposure time to UV light and heat) could impact mechanical properties and biocompatibility of Dental LT resin [53, 89]. Further studies both in vitro and in vivo are needed based on these claims to test this resin and other direct aligner printable resins [90].
With the evolution of materials, the direct printing of aligners will take over the thermoforming process, save a considerable amount of models resin, streamline production, and reduce costs [91].
4.5 Thermoforming aligners
4.5.1 Influence of thermoforming
Ruy et al. examined the impact of thermoforming on the physical and mechanical properties of various thermoplastic materials for clear aligners (Duran, Essix A+, Essix ACE, and eCligner). They observed that the optical transparency, the tensile force, and the elastic modulus of the aligner materials decline after the thermoforming process, while water absorption was increased [92].
Moreover, they recommended evaluating these materials’ durability after thermoforming to characterize their properties for their clinical application [92]. From a clinical perspective, the authors also proposed choosing the polymers depending on the treatment required, as some of them show a significant decrease in flexural strength after thermoforming and exhibit permanent deformation during treatment. On the other side, the application of large forces to the teeth can lead to absorption of the apical root [92].
Kwon et al. [51] assessed the force delivery properties of thermoplastic orthodontic materials. They found that the forces delivered by thin materials were more significant than those delivered by thick materials of the same brand [92].
4.5.2 Esthetic appearance
Transparency is evaluated to investigate the esthetic aspect of the materials. The transparency of materials decreased with an increase in their thickness. In addition, with decreased thickness after thermoforming, the transparency also decreased, which can be explained by the structural deformation of thermoplastic materials resulting in decreased transparency. Nevertheless, this transparency change did not compromise the esthetic appearance of clear aligners [92].
Many studies evaluate the stability of the materials after their average use of two weeks through the colorimetric alterations of aligners [93]. Bernard et al. affirm that there are foods that stain more than others (above all black tea) and that the Invisalign aligners (TPU) were more prone to pigmentation than the ClearCorrect (PU) or the Minor Tooth Movement devices (PET-G) after exposure to coffee or red wine. Black tea caused important stains on the surface of the three tested brands [93, 94].
4.5.3 Water absorption
Water absorption can negatively influence the mechanical properties of polymers leading to irreversible deterioration because water absorption is often appended to swelling and, thus, a deterioration of the polymers [95, 96]. Besides the deterioration effect, the swelling also leads to dimensional variations of the mouth devices, which affects the orthodontic forces [96]. Therefore, an ideal thermoplastic material for a clear aligner should have a low water absorption [54].
Tamburino et al. investigated the properties of materials for the thermoforming production of aligners. The materials used in their study were: Duran® (PETG, Sheu dental GmbH), Biolon (PETG, Dreve Dentamid GmbH) and Zendura® (PU, Zendura Dental). Artificial saliva was used as an aging agent at a temperature of 37°C for 7 days [97]. The liquid absorption of Duran material is only almost half of the Zendura one. In addition to higher water uptake, the authors observed a decline of the mechanical properties of the Zendura that can be related to the mechanism of intramolecular bond destruction by water molecules [97].
Ryokawa et al. [8] reported that water absorption by both PETG and copolyester increased to 0.8 wt% in their 2-week experiment. In addition, water absorption by PETG differed depending on the type of thermoplastic material [55]. Zhang et al. [93] reported that water absorption increased when polyurethane was added to PETG during the development of new thermoplastic material for thermoformed aligners [92].
4.5.4 Mechanical properties
Tamburino et al. investigated the mechanical properties of the aligner materials Duran, Biolon, and Zendura in the as-delivered state, after thermoforming, and after storage in artificial saliva [97]. The authors found that the tensile yield stress of the Duran and Biolon materials only slightly changed after thermoforming (9% increase for Duran, 6% decrease for Biolon), while it decreased by one-third for the Zendura [54]. After exposure to artificial saliva, the tensile yield stress of the Duran material decrease back to it as-supplied strength, while the tensile yield stress of Biolon and Zendura materials slightly increase (to −3% respectively to −28%). Based on their finding, these authors propose to select a material for orthodontic devices after characterizing its mechanical properties after the corresponding manufacturing process and storage test in an intraoral simulation environment [54].
4.5.5 Elastic modulus
A higher elastic modulus is beneficial for aligners as it increases the force delivery capacity of the aligner under constant strain [98, 99]. Plus, materials with a higher elastic modulus can produce the same forces from thinner thickness [99]. The elastic modulus is proportional to the material stiffness. In their study [97], Tamburino et al. also examined the elastic modulus of the aligner materials Duran, Biolon, and Zendura in the as-delivered state, after thermoforming and after storage in artificial saliva. The elastic modulus of the Duran and Zendura materials increased by 11% respectively 17% after thermoforming, while the one of the Biolon material falls by 7%. Looking at the elastic modulus after artificial saliva exposure of the materials shows different behavior [100]. The elastic modulus of Biolon and Zendura material is relatively stable, while a significant decrease was observed for Duran. This decrease can be explained by water uptake happening during the storage in artificial saliva fluid [54].
5. Conclusions
Practice owners need to invest in material resources, but they also need to invest in education to help their team implement homemade aligner workflow. While 3D printing aligners in-house require that practices invest time and money, eliminating lab fees and the ability to provide same-day high-quality, consistent services justifies the investment by increasing profit margins, decreasing treatment timelines, and improving patient satisfaction. In-house production of aligners is the best option for practices that want more profitable and faster service. It just requires flexibility and an openness to learning new workflows that will carry the practice forward.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"digital workflow, orthodontics, aligner, thermoforming, 3D Printing, facial scan, planning software, homemade aligners",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/79680.pdf",chapterXML:"https://mts.intechopen.com/source/xml/79680.xml",downloadPdfUrl:"/chapter/pdf-download/79680",previewPdfUrl:"/chapter/pdf-preview/79680",totalDownloads:221,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"September 7th 2021",datePrePublished:"December 16th 2021",datePublished:"August 17th 2022",dateFinished:"December 16th 2021",readingETA:"0",abstract:"Advanced digital technology is rapidly changing the world, as well as transforming the dental profession. The adoption of digital technologies in dental offices allied with efficient processes and accurate high-strength materials are replacing conventional aligners workflows to improve overall patients’ experiences and outcomes. Various digital devices such as 3D printers, intraoral and face scanners, cone-beam computed tomography (CBCT), software for computer 3D ortho setup, and 3D printing provide new potential alternatives to replace the traditional outsourced workflow for aligners. With this new technology, the entire process for bringing clear aligner production in-office can significantly reduce laboratory bills and increase patient case acceptance to provide high-quality and customized aligner therapy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/79680",risUrl:"/chapter/ris/79680",signatures:"Dalal Elmoutawakkil and Nabil Hacib",book:{id:"10780",type:"book",title:"Current Trends in Orthodontics",subtitle:null,fullTitle:"Current Trends in Orthodontics",slug:"current-trends-in-orthodontics",publishedDate:"August 17th 2022",bookSignature:"Farid Bourzgui",coverURL:"https://cdn.intechopen.com/books/images_new/10780.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-964-1",printIsbn:"978-1-83969-963-4",pdfIsbn:"978-1-83969-965-8",isAvailableForWebshopOrdering:!0,editors:[{id:"52177",title:"Prof.",name:"Farid",middleName:null,surname:"Bourzgui",slug:"farid-bourzgui",fullName:"Farid Bourzgui"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"425010",title:"Dr.",name:"Dalal",middleName:null,surname:"elmoutawakkil",fullName:"Dalal elmoutawakkil",slug:"dalal-elmoutawakkil",email:"dr.elmoutawakkil@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"425018",title:"Dr.",name:"Nabil",middleName:null,surname:"Hacib",fullName:"Nabil Hacib",slug:"nabil-hacib",email:"nhacib@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1 Data acquisition",level:"2"},{id:"sec_2_3",title:"2.1.1 Digital model creation",level:"3"},{id:"sec_3_3",title:"2.1.2 3D Facial scan",level:"3"},{id:"sec_3_4",title:"2.1.2.1 3D dentofacial integration",level:"4"},{id:"sec_5_3",title:"2.1.3 3D X-ray: Cone-beam CT",level:"3"},{id:"sec_5_4",title:"2.1.3.1 Procedure",level:"4"},{id:"sec_8_2",title:"2.2 Digital treatment planning",level:"2"},{id:"sec_8_3",title:"2.2.1 Automatic segmentation",level:"3"},{id:"sec_9_3",title:"2.2.2 Realtime simulation",level:"3"},{id:"sec_10_3",title:"2.2.3 Advanced staging and sequencing",level:"3"},{id:"sec_11_3",title:"2.2.4 Attachments",level:"3"},{id:"sec_12_3",title:"2.2.5 Ready to print models",level:"3"},{id:"sec_13_3",title:"2.2.6 Automatic pontics for concealing gaps and missing teeth",level:"3"},{id:"sec_14_3",title:"2.2.7 Variable trim line",level:"3"},{id:"sec_16_2",title:"2.3 3D Printing",level:"2"},{id:"sec_16_3",title:"2.3.1 Fused deposition modeling (FDM) 3D printing",level:"3"},{id:"sec_17_3",title:"2.3.2 Stereolithography 3D printing",level:"3"},{id:"sec_17_4",title:"2.3.2.1 Dental model resin",level:"4"},{id:"sec_18_4",title:"2.3.2.2 Dental long term (LT) ® clear resin",level:"4"},{id:"sec_19_4",title:"2.3.2.3 Tera Harz TC-85",level:"4"},{id:"sec_20_4",title:"2.3.2.4 Post treatment",level:"4"},{id:"sec_23_2",title:"2.4 Thermoforming aligners",level:"2"},{id:"sec_23_3",title:"2.4.1 Thermoforming machines",level:"3"},{id:"sec_25_2",title:"2.5 Tray trimming and polishing",level:"2"},{id:"sec_25_3",title:"2.5.1 Scalloped VS continuous curve",level:"3"},{id:"sec_27_2",title:"2.6 Packaging and delivery",level:"2"},{id:"sec_28_2",title:"2.7 Delegation",level:"2"},{id:"sec_30",title:"3. Results",level:"1"},{id:"sec_31",title:"4. Discussion",level:"1"},{id:"sec_31_2",title:"4.1 Digital model creation",level:"2"},{id:"sec_32_2",title:"4.2 Facial scan",level:"2"},{id:"sec_33_2",title:"4.3 Setup planning",level:"2"},{id:"sec_34_2",title:"4.4 3D printing",level:"2"},{id:"sec_35_2",title:"4.5 Thermoforming aligners",level:"2"},{id:"sec_35_3",title:"4.5.1 Influence of thermoforming",level:"3"},{id:"sec_36_3",title:"4.5.2 Esthetic appearance",level:"3"},{id:"sec_37_3",title:"4.5.3 Water absorption",level:"3"},{id:"sec_38_3",title:"4.5.4 Mechanical properties",level:"3"},{id:"sec_39_3",title:"4.5.5 Elastic modulus",level:"3"},{id:"sec_42",title:"5. Conclusions",level:"1"},{id:"sec_46",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Robertson L, Kaur H, Fagundes NCF, Romanyk D, Major P, Flores Mir C. Effectiveness of clear aligner therapy for orthodontic treatment: A systematic review. Orthod Craniofac Res. 2020 May;23(2):133-142. 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Effect of print orientation and duration of ultraviolet curing on the dimensional accuracy of a 3-dimensionally printed orthodontic clear aligner design. Am. J. Orthod. Dentofac. Orthop. 2020, 158, 889-897'},{id:"B90",body:'Tartaglia GM, Mapelli A, Maspero C, Santaniello T, Serafin M, Farronato M, Caprioglio A. Direct 3D Printing of Clear Orthodontic Aligners: Current State and Future Possibilities. Materials (Basel). 2021 Apr 5;14(7):1799. DOI: 10.3390/ma14071799'},{id:"B91",body:'http://itgraphy.com/download/eng/TC-85DAC.pdf'},{id:"B92",body:'Ryu JH, Kwon JS, Jiang HB, Cha JY, Kim KM. Effects of thermoforming on the physical and mechanical properties of thermoplastic materials for transparent orthodontic aligners. Korean J Orthod 2018;48:316-325'},{id:"B93",body:'Putrino A, Barbato E, Galluccio G. Clear Aligners: Between Evolution and Efficiency-A Scoping Review. Int J Environ Res Public Health. 2021 Mar 11;18(6):2870. DOI: 10.3390/ijerph18062870'},{id:"B94",body:'Bernard G, Rompré P, Tavares JR, Montpetit A. Colorimetric and spectrophotometric measurements of orthodontic thermoplastic aligners exposed to various staining sources and cleaning methods. Head Face Med. 2020 Feb 18;16(1):2. DOI: 10.1186/s13005-020-00218-2'},{id:"B95",body:'Zhang N, Bai Y, Ding X, Zhang Y. Preparation and characterization of thermoplastic materials for invisible orthodontic. Dental Materials Journal. 2011; 30: 954-059'},{id:"B96",body:'Boubacri A, Elleuch K, Guermazi N, Ayedi HF. Investigations on hygrothermal aging of thermoplastic polyurethaane material. Materials and Design. 2009; 30: 3958-3965'},{id:"B97",body:'Tamburrino F, D’Anto V, Bucci R, Alessandri-Bonett G, Barone S, Razionale AV. Mechanical properties of thermoplastic polymers for aligner manufacturing: in vitro study. Dentistry Journal. 2020; 8: 47'},{id:"B98",body:'Lombardo L, Palone M, Longo M, Arveda N, Nacuchi M, Pascalis FD, et al. 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\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
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He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. 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Graduate in Sciences (Chemist), graduate in Geography and History (Geography), master in Water Management, Treatment, master in Fertilizers and Environment and master in Environmental Management; Ph.D. in Environmental Sciences. His research is focused on soil-water and waste-environment relations, mainly on soil-water and soil-waste interactions under different management and waste reuse. His work is reflected in more than 230 communications presented in national and international conferences and congresses, 29 invited lectures from universities, associations and government agencies. 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He was also invited to serve as an associate editor for special issues of the Journal of the American Water Resources Association. He has served as an editorial member for international journals such as Hydrology, Journal of Ecology & Natural Resources, and Hydro Science & Marine Engineering, among others. He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. 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He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. He has published about ninety scientific papers in peer-reviewed international journals and several papers in national and international conferences. He participated as an invited speaker at thirty international conferences. Prof. Rashed is the editor-in-chief and an editorial board member for several international journals in the fields of chemistry and environment. He is a member of several national and international societies. He received the Egyptian State Award for Environmental Research in 2001 and the Aswan University Merit Award for Basic Science in 2020. 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Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. 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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. 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He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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\r\n
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A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:126,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},subseries:[{id:"3",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"
\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
",annualVolume:11399,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",annualVolume:11400,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"302145",title:"Dr.",name:"Felix",middleName:null,surname:"Bongomin",fullName:"Felix Bongomin",profilePictureURL:"https://mts.intechopen.com/storage/users/302145/images/system/302145.jpg",institutionString:null,institution:{name:"Gulu University",institutionURL:null,country:{name:"Uganda"}}},{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",institutionString:"Islamic Azad University, Tehran",institution:{name:"Islamic Azad University, Tehran",institutionURL:null,country:{name:"Iran"}}}]},{id:"5",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",annualVolume:11401,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},{id:"6",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",annualVolume:11402,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",fullName:"Emmanuel Drouet",profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",institutionString:null,institution:{name:"Grenoble Alpes University",institutionURL:null,country:{name:"France"}}},{id:"188219",title:"Prof.",name:"Imran",middleName:null,surname:"Shahid",fullName:"Imran Shahid",profilePictureURL:"https://mts.intechopen.com/storage/users/188219/images/system/188219.jpeg",institutionString:null,institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"214235",title:"Dr.",name:"Lynn",middleName:"S.",surname:"Zijenah",fullName:"Lynn Zijenah",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEJGQA4/Profile_Picture_1636699126852",institutionString:null,institution:{name:"University of Zimbabwe",institutionURL:null,country:{name:"Zimbabwe"}}},{id:"178641",title:"Dr.",name:"Samuel Ikwaras",middleName:null,surname:"Okware",fullName:"Samuel Ikwaras Okware",profilePictureURL:"https://mts.intechopen.com/storage/users/178641/images/system/178641.jpg",institutionString:null,institution:{name:"Uganda Christian University",institutionURL:null,country:{name:"Uganda"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/167287",hash:"",query:{},params:{id:"167287"},fullPath:"/profiles/167287",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()