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For reliable communication, binary hypothesis testing is important to find the error probability. The interest has been growing in short and medium blocklengths also called short packets to implement in the modern day wireless communication system. The colonoscopy diagnosis now uses mmWave which is 5G and 6G. This is utilised for design models to enhance the image technology in the diagnosis of colonoscopy, and endoscopy to facilitate medical practitioners. There is a possibility to use these techniques in medical equipment for real-time support to physicians and operator-independent prediction. The 5G and eventually 6G would enable the expansion to faster processing of data analysis and medical imaging technology.
*Address all correspondence to: ksa2te@virginia.edu
1. Introduction
The colonoscopy and endoscopy are related to a process of checking the colon. The two are about the internal organs and radiographic visualisation is done by an endoscope. The colonoscopy itself is an endoscopy and it’s a nonsurgical procedure. One should note that the visualisation of internal organs is important as far as diagnosis is concerned. Further, it’s necessary to make sure nothing extra is developed there and the colon is clean. In this process, a physician will look into it and examine the colon to identify if anything abnormal is growing. Hence, it can be easily cured. As per statistical data, bowel cancer is one of the biggest cancers killers in the United States. The diagnosis done earlier is better to have a proper cure. In the United States, there are several clinics using state-of-the-art technology which is the main topic here. This is where 5G, 6G or mmWave are used which is the latest technology. The usage of equipment and facilities with the latest technology provides a doctor to analyse efficiently in case there are polyps, or other objects showing an abnormal growth in the organ. Colorectal cancer (CRC) may develop in the colon or the rectum as a noncancerous adenomas or polyps over several years. All these precancerous growths can be removed, thus reducing the risk of developing a colon cancer.
This is one of the cancers that is preventable in full if detected, and treated early. The current diagnoses include colonoscopy and upper endoscopy. In general, it’s an invasive examination that looks for small growths called polyps within the colon. These procedures are uncomfortable and are in demand. Polyps may not necessarily lead to cancer, but current methods of colonoscopy miss around one (1) in five (5) of them due to old procedures.
The other way to put it is that, there could be a possibility that cancerous polyps are not found despite diagnostic procedures. One of the good places to read a detailed summary about colon cancer, its diagnosis and a procedural treatment will be the web-site of the American Cancer Society and the reference [1].
It has been known that our health-care system is experiencing a tremendous amount of pressure to complete the number of endoscopies required, and also due to the fact that the bowel cancer screening age has been lowered. Further, the appointments were reduced due to the pandemic, now there is a backlog. An individual who is sixty years old or above must take an appointment ahead of time so that he/she gets an opportunity to take care of the endoscopy procedure. The recommendation by the Department of Health and Human Services is the age of sixty and above including the screening for colorectal cancer would be helpful. The colorectal cancer has two parts, one is colon cancer and the other is rectal cancer. Both are equally important.
Research and development (R & D) are the keys to solving several issues the health-care system has. The health-care providers can offer a better service with a higher quality of care to their patients by making use of the new technology solutions. This may include the community services and/or at home. Nowadays, remote diagnosis is also in place. Table 1 shows the list of one of the topmost clinics in the United States. One may consider using other local clinics and also ask for a referral.
In other words, it’s possible to do home screening for this type of colorectal cancer. The usage of AI (artificial intelligence) assisted colonoscopy polyp detection trial will help doctors to improve the quality of patient care, improve the accuracy of detection rates by capturing the information correctly and eventually reduce errors. This kind of method would significantly improve the patient outcomes when assisted in proper and timely diagnoses. Moreover, it will save time for the endoscopy procedure. As far as the cost is concerned, one must enquire by health-care provider.
On the other hand, many clinics still use the older methodology of manual procedure. For example, colon cancer is detected through colonoscopy procedure that is manual requiring extra attention and time from medical practitioners for accurate detection. Further, this is a longer way to do the detection and not at all comfortable one for patients including for the doctors and nurses who have to perform the procedure with an old method. The AI (artificial intelligence) assisted and “Ultra-fast, low latency 5G networks will transform the Health-care sector”. For example, at Airtel, India they have demonstrated this by conducting first colonoscopy trials. Health-care is one of the most promising use cases for 5G, and they collaborated with Apollo Hospitals, India.
Here in the United States, AWS, HealthNet Global are partnering with clinics and hospitals for the usage of the latest technology. Several clinics are already using the ultra-fast 5G technology. The usage of mmWave which will be even faster is in progress. This would certainly assist the doctor’s ability to detect. AI usage has helped to improve physician’s accuracy in detecting the chronic illnesses and detecting the growth of cancerous cells if any. Early detection and removal of polyps can easily be avoided so that any extra tumour may not become cancerous.
Mayo Clinic and several other clinics have a patient-centric approach that keeps them on an outlook for technologies that can make the outcomes better. With this new technology, one can develop a battery-free communication system for a wireless video capsule endoscope with potential video streaming at a rate of up to fifteen (15) Mega bits per second. An application of an innovative approach by using back-scatter for implants, and a RADAR approach that can remotely read the information from the deep implants, such as the video capsule endoscope. This is used with a 5G deployed network together with cutting-edge computing. Eventually, it should be capable of transmitting the video data from the capsule to a high-performance computing platform in a secure manner. This provides an end-to-end latency to perform polyp detection and its localisation.
This energy-intensive inference by the use of deep learning neural networks technique for polyp detection and localisation can be done in the edge where control signals are sent back to the pill if being used by a doctor to increase the spatial and temporal resolution of the video. Thus, one obtains high-quality images for further analysis. Please see references [2, 3, 4]. The access to near real-time data and the ability to make split-second decisions are critical and important in health-care environments. This important sector has the potential to achieve excellent benefits from 5G and 6G or mmWave advanced technologies when implemented as per the needs of a patient as well as the doctors and nurses. This kind of better communication will certainly produce efficiencies in the health-care sector. Diagnostics should be done faster at a fast pace to save time in the diagnosis and treatment of a patient. Transfers of massive files, images and other content will benefit from low latency for fast data transfer. The computer power embedded with 5G and beyond will help to accelerate benefits as we progress with the usage of the latest technology in health-care system. The reader is referred to [3, 5, 6].
We describe the method of artificial neural network simulation for a given data-set, [7] and the results obtained with respect to the survival rate. The simulation results can be compared with [2, 3, 5, 6, 8]. This is the part of machine learning and in part known as deep learning. This is also known as AI (artificial intelligence) assisted program for health-care systems.
The rapid growth of technology usage in research changes and with the AI-assisted research shows promising results. One needs testing of AI models for a complex system and diagnosis of colonoscopy and endoscopy. The result is acceptable with the analysis of training and testing in this work. This is one of the techniques of deep learning and machine learning.
For histology, it should be mentioned that instead of data, one can use images and work with them to do the deep learning analysis. Digital imaging technology also belongs to 5G, 6G and is of utmost importance to physicians. Thus providing operator-independent pathology prediction. With machine learning and deep learning techniques, one can develop an algorithm as in [2, 5, 9].
2.1 Simulation results
The simulation results are obtained by using the data-set mentioned in the previous section for the analysis of colorectal cancer in males and females. Males may have some form of colon cancer on the right while females may have it on the left of the colon.
Figure 1 shows the results of the rate of survival and frequency of survival.
Figure 1.
Average male female DFS.
Figure 2 shows the results of the training of output and the target values. In this figure, the results indicate that each training epoch uses a shaded background. An epoch is a full pass through the entire data-set. It shows that there are nine males on the left and one female on the right. The validation frequency is approximately 87 percent.
Figure 2.
Validation result.
2.2 Data-set used
In this study, we are using data-set from Kaggle’s web-site for colorectal cancer which was published in 2021. The data-set can be accessed from the reference list [7]. The data-set describes about male, female real colorectal cancer, for health and cancer data analysis. Their analysis is based on probability and histogram charts. Here it’s done using MATLAB’s method of training and testing model. They also have other data-set that can analyse histology by doing the training and testing model of deep learning with MATLAB toolbox or by using Python language. Please see the references [3, 4, 5, 6, 9].
By the use of 5G and remote technology, an innovative new pilot scheme has been launched in the United Kingdom as well as here in the United States at the Mayo Clinic and several other clinics. West Midlands aims to give patients the ability to undergo a procedure to detect the causes of digestive or stomach complaints in the privacy of their homes. Please see Table 1 for the list of top ten (10) clinics.
Patients awaiting an endoscopy (a procedure whereby a camera is fed into the bowel through a thin tube to detect signs of issues such as cancer) will soon be able to undertake a similar, less invasive procedure from the comfort of their own homes. It’s a matter of time before the 5G capability will provide changes to future usage of AI (artificial intelligence).
Further, 6G or mmWave technology will improve with ultra-high speed and low latency in helping clinicians, and medical practitioners to analyse the data, images and video footage recorded. Hence, it will be even quicker identification of polyps, the precursors to cancer and other irregularities, than currently possible.
3.1 An example of AI technology
This is where the MK5G project can be inserted. The MK5G: Connecting Communities Test-bed is leading in the United Kingdom as well as here in the United States in demonstrating how applications of 5G technology can be implemented in a real-world setting. This in turn will improve services within the health-care system. The MK5G project aims to raise the bar for standards of health-care across the Europe and across the Atlantic here in the United States in urban areas with access to connectivity required.
In the United States, researchers at the Mayo Clinic have been investigating the usage of artificial intelligence which would increase polyp detection. In general, gastroenterologists are engaging AI as a tool to improve care for a wide range of health conditions which would help to find elusive signs earlier when the diseases are easily treatable. Thus, in turn will improve the quality of life of a patient. With AI assistance, in the case of colon cancer, the AI system is capable of working alongside the physician in real-time. It can scan the colonoscopy video feed and can draw small, red boxes around polyps that might otherwise be overlooked. There are many clinics here in the United States that use this latest technology of AI-assisted. The invention of this technology enables an “extra helping hand and an extra pair of eyes” for physicians, and staff. This helps to improve the detection rate of polyps, thus saving lives and vastly improving patient care. The data was processed by Avesha edge inferencing applications on AWS platforms in real-time resulting in much faster analysis, the company said in a statement.
Magnetically controlled capsule endoscopy (MCE), with equally favourable diagnostic accuracy as conventional gastroscopy, has become an efficient and comfortable diagnostic modality for GI (gastrointestinal) diseases. An endoscopist could control the movement of the capsule inside the human body precisely through the manipulation of the magnetic robot arm. The non-invasive capsule endoscope, which allows image acquisition after being swallowed, works separately from the control parts of the MCE system. The separable and robotic characteristics of the MCE system provide the technical foundation for remote operation. Moreover, the recent development of the fifth generation of wireless systems (5G), with its high speed, low latency and wide bandwidth, has further supported real-time tele-medicine with reliable networks.
Some real-time remote examinations and surgeries, such as tele-ultrasound, tele-robotic spinal surgery and laparoscopic tele-surgery, have been explored and successfully carried out. A 5G-based remote MCE system is such that the remote endoscopist can directly perform the MCE examination on the patient through a remote-control system and the application of a 5G network. This study aimed to evaluate the feasibility and safety of the 5G-based remote MCE system. Communications solutions provider Bharti Airtel and Apollo Hospitals have carried out India’s first 5G-driven, artificial intelligence (AI) guided colonoscopy trials. Please see references [2, 9, 10]. Similar work has been done at the Mayo Clinic and several other topmost clinics, and hospitals here in the United States.
In general, as per current protocol, colon cancer is to be detected through a colonoscopy method which is manual and painful. Moreover, patients are reluctant to go through such a procedure. This procedure is performed using a device comprising a light, and flexible tube with light, camera and tools at one end, which are used to extract samples to identify an infected polyp. This method is long, and is discomforting for patients including the doctors and nurses who should perform this procedure which takes around 30 to 40 minutes per case. So, a change with the usage of new technology is helpful and it’s becoming available now.
With the new technology of AI-guided colonoscopy procedure, the image processing happens in real-time without any lag even when the physician moves the scope through the colon for it to be overlaid on top of the right element of the colon. In other words, the advent of this technology enables a physician to improve the detection rate of polyps, significantly improving the patient care. An AI-assisted colonoscopy polyp detection trial will be helpful for doctors to improve the quality of patient care, improve the accuracy of detection rates by capturing the information correctly and reduce the error probability.
3.1.1 New technology wins real-time
The capsule endoscopy is considered to be a very safe method for gastrointestinal tract examination. The capsule is mainly excreted with a patient’s faeces within 24–48 hours after ingestion. There has been a report of retention of the capsule lasting almost four and a half years although the patient was asymptomatic and did not feel well. However, the risk of bowel obstruction may be countered by an abdominal X-ray to locate the device for removal by endoscopy or surgery. Laser surgery is also an option.
With the introduction of the NaviCam® Stomach Capsule System, an advanced technology has been introduced. This combines the magnetic control with innovative and intelligent software to give medical practitioners external robotic control of the capsule inside the human body. One should know that it’s a safe procedure with no extra surgery needed. This is one of the minimally invasive procedures by the use of NaviCam®. This system is guided into real-time with several dimensions (two rotational and three translational planes) by an operator from either making use of a control console or a remote console. Thus, multi-centre blinded study, the NaviCam® Stomach System is considered to be a safe method of visualising the gastric mucosa by the usage of remote magnetic manipulation and it would not require any more need for intubation or sedation (Table 2).
The NaviCam® Stomach System can be used in clinics and hospitals both, including the ER (Emergency Room) setting. The ANN (artificial neural network) and CNN (convolutional neural network) diagnostic program systems have shown a good performance in diagnosing gastric focal lesions in MCE (Magnetically controlled capsule endoscopy) images. For the full article, please refer to [2, 5, 6, 7, 8].
3.1.2 Data table for the figure used to analyse male female survival rate
Table 2 shows the rate of survival of colon cancer patients. Both males and females are considered. This table shows the results obtained via simulation of the data by using the method of ANN (artificial neural network). This provides a training output and the target intended to obtain the best results possible to compare the male and female patients who survived the colon cancer.
3.1.3 Ten best clinics for colonoscopy and gastronomy in the United States
In Table 1, the top ten best clinics and hospital lists are provided here in the United States. The ranking is calculated using percent calculation in terms of the performance of the clinics and hospitals. There are at least ten more excellent clinics. One may find if searched via the internet by using reference [1]. For example, John Hopkins University medical centre is one of the best clinics as well. It depends to whom a patient believes in. Even a small-town clinic may provide a good diagnosis. Obviously, they may not have the latest technology of AI usage.
We provide simulation results by using ANN (artificial neural network) by training and testing methodology including the review of the similar work as shown in [2, 3, 5, 9, 11].
It is to be noted that the number of colorectal cancer cases in the United States has been decreasing since the mid-1980s. During the 2000s, incidence rates dropped from three percent to four percent each year. This was due to increased screening in adults aged 50 and older. From 2011 to 2019, incidence rates continued to decrease by one percent annually. However, incidence has been rising by one percent to two percent each year in younger people since the mid-1990s. It is estimated that the colorectal cancer is one of the fourth most commonly diagnosed cancer in the United States among men and women aged 30 to 39. This gives an idea that there is a need for new technology implementation and diagnostic treatment for CRC.
If proven, the 5G and beyond which is 6G or mmWave capability could one day be paired with AI technologies to help clinicians to analyse the images, video footage and video recorded. This in turn would mean even faster identification of polyps, in comparison to currently possible through manual review.
Clinical Robotic and /or tele-surgery (or remote surgery) is aimed at providing high-quality health-care in the most complex medical interventions and surgeries. Highly-qualified medical expertise will be transferred from the major hospitals to the decentralised ones with the use of remote-surgery, remote diagnostics and tele-medicine resulting in significant cost reduction, and improved efficiency in health-care services. Please read the web-site of clinics that have these options or visit the web-site of the American Cancer Society and reference [1] and also at the topmost clinic list from Table 1.
Tele-surgery, where parts of the procedure are controlled by a surgeon from a central site to a remote location, is the most demanding application among the remote health-care services and thus by successfully validating this application, the validation of technology for the entire range of less demanding remote health-care applications that can be implied.
Before 5G, only a few tele-surgeries were carried out and reported by the use of a 4G network. Otherwise, mostly internet and satellite networks were previously used for tele-surgery. A robotic tele-surgery was performed to complete a pituitary tumour resection on a simulated model over the internet with a bandwidth of 1 Giga byte per second in 2015. One can visit to see which clinic or hospital did this kind of surgery at reference [1]. Further, a robotic tele-surgery was performed in the left internal mammary artery dissection in pigs through a satellite network with a maximum bandwidth of 10 Mega bytes per second.
The integration of human and machine generated data will radically change health-care services. Please see the latest data-set at reference [7]. In order to accommodate these health-care needs, an entire communication infrastructure integrating the IoT (internet of things) repositories, AI, super-computing, innovative computational algorithms and edge computing micro-sensors would be needed. This will include processing at the point of data acquisition.
Hence, it will be required to construct or create a “telecommunication ecosystem”, which will not only be able to archive, monitor and optimise current activities, but also be used to estimate future trends in the personalised medicine, together with an overall health-care services.
At the moment there are research projects investigating such potential systems that are being implemented on an experimental basis. In the forthcoming future, there is a possibility that the bathroom may also become extensively populated with all types of sensors for automatically monitoring health status, and providing a complete physical examination to update health status on a daily basis, while the person is simply performing the normal daily bathroom activities.
In the subsection, appendix A, a flow chart provides a good idea about the process of colorectal cancer treatment. A figure is also added there and it has been obtained from the web-site called Figure-Fit. Besides that, a short video link will be provided for this chapter on colonoscopy with the latest technology usage as mentioned in this subsection at the end.
In this work, we have analysed the colorectal cancer by using an artificial neural network with a data-set obtained from Kaggle’s web-site. A flowchart is being added that shows a basic idea of colonoscopy treatment with 5G, 6G or mmWave technology.
The next Figure 4 in the form of a flowchart also shows how the colon cancer develops and can be treated.
Figure 4.
Colon cancer development, treatment.
Video materials
A video of this chapter is submitted separately. It will be featured as a link inside the text as ColonmmWaveKSA.mp4 which does exceed to 100 MB of limit. The video link will be placed in Appendix A.
Polyposis Coli (APC)] A multi-functional tumour suppressor gene. Mutations in this gene are responsible for familial adenomatous polyposis and contributes to many sporadic colorectal cancers.
Bowel cancer
Cancer of the large bowel; also known as colorectal cancer, colon cancer or rectal cancer.
Colon
Part of the large intestine that extends from the end of the small intestine (cecum) to the rectum.
Colonoscope
Flexible, elongated tube that can be inserted through the anus and passed through the colon allowing visualisation of the inside.
Colonoscopy
Visual examination of the inner surface of the colon by means of a colonoscope
Colostomy
Procedure to create an opening of the colon through the skin of the abdomen to allow for the passage of faeces; also, the opening itself.
CLE
An endoscopy procedure that uses a specialised endoscope capable of visualising the mucosal layer of the colon at very high magnification.
CT Graphy
Also known as virtual colonoscopy, a medical imaging procedure that uses low dose radiation computerised tomography (CT) scanning to obtain an interior view of the colon (the large bowel) that is otherwise only seen with a more invasive procedure such as colonoscopy where an endoscope is inserted into the rectum and passed through the entire colon.
ABS
Artificial bowel sphincter
ANN
Artificial Neural Network
CI
Confidence Interval
CNN
Convolutional Neural Network
CISNET
Cancer Intervention and Surveillance Modelling Network
COCOS
Colonoscopy or Colonography for Screening
C-RADS
Colonography Reporting and Data System
CRC
Colorectal cancer
CRC-SPIN
Colorectal Cancer Simulated Population Model for Incidence and Natural History
CT
Computed tomography
DFS
Disease-Free Survival
ESGAR
European Society of Gastrointestinal and Abdominal Radiology
2.Yang T, NingLiang JL, Young Y, Li Y, Huang Q , Li R, et al. Intelligent imagining Technology in Diagnosis of colorectal cancer using deep learning. Journal IEEE Access. 2019;7:178839-178847. DOI: 10.1109/ACCESS.2019.2958124. [Accessed: December 23, 2019]
3.Kather J, Weis CA, Bianconi F, et al. Multi-class texture analysis in colorectal cancer histology. Scientific Reports. 2016;6:27988. DOI: 10.1038/srep27988
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5.Yang T, NingLiang JL, Young Y, Li Y, Huang Q , Li R, et al. 5G-based RemotColorectal cancer facts figures 2020-2022e magnetically controlled capsule endoscopy for examination of the stomach and small bowel. United European Gastroenterology Journal. 2022;11. DOI: 10.1002/ueg2.12339. [Accessed: 13 October 2022]
6.Colorectal Cancer Facts & Figures 2020-2022. Available from: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/colorectal-cancer-facts-and-figures/colorectal-cancer-facts-and-figures-2020-2022.pdf [Accessed: November 04, 2016]
7.Available from: https://www.kaggle.com/datasets/amandam1/colorectal-cancer-patients [Accessed: November 2021]
8.Morgado-Diaz JA, editor. Gastrointestinal Cancers. Brisbane, Australia: Exon Publications; Baojun Duan, Yaning Zhao et al, Chapter 1. Available from: https://www.ncbi.nlm.nih.gov/books/NBK586003. [Accessed: September 30, 2022]
9.Mahmood S, Ghazel T, Khan M, Zubair M, Naseem M, Faiz T, et al. Malignancy detection in lung and colon Histopathalogy Imgaes using transfer learning with class selective image processing. Journal IEEE Access. 2022;10:25657-25668. DOI: 10.1109/ACCESS.2022.3150924. [Accessed: March 10, 2022]
10.Airtel, Apollo, AWS conduct India’s first 5G-driven, AI-guided colonoscopy trial. Available from: https://www.teleinfotoday.com/press-releases/airtel-apollo-aws-conduct-indias-first-5g-driven-ai-guided-colonoscopy-trial [Accessed: July 26, 2023]
11.Waye JD, Rex DK, Williams CB, editors. Colonoscopy: Principles and Practice. Wiley-Blackwell. 2nd edition. 2009. [Accessed: 2003]. ISBN: 978-1-405-17599-9
Written By
Kumud S. Altmayer
Submitted: 06 September 2023Reviewed: 26 September 2023Published: 13 March 2024
Open access peer-reviewed chapter
