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Mubarak M, Rashid R, Sapna F, Shakeel S. Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology. Artif Intell Gastroenterol 2024; 5:91550. [DOI: 10.35712/aig.v5.i2.91550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 07/06/2024] [Accepted: 07/29/2024] [Indexed: 08/08/2024] Open
Abstract
Digital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.
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Affiliation(s)
- Muhammed Mubarak
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
| | - Rahma Rashid
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
| | - Fnu Sapna
- Department of Pathology, Montefiore Medical Center, The University Hospital for Albert Einstein School of Medicine, Bronx, NY 10461, United States
| | - Shaheera Shakeel
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
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Abstract
The emergence of Big Data has been facilitated by technological advancements in the processing, storage, and analysis of large quantities of data. Its strength is derived from its size, ease of access, and speed of analysis, and it has enabled surgeons to investigate areas of interest that traditional research models have historically been unable to address. In the future, Big Data will likely assist in the incorporation of more advanced technologies into surgical practice, including artificial intelligence and machine learning to realize the full potential of Big Data in Surgery.
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Affiliation(s)
- Christopher Prien
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Eddy P Lincango
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Stefan D Holubar
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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Nazarian S, Koo H, Carrington E, Darzi A, Patel N. The future of endoscopy – what are the thoughts on artificial intelligence? J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2178516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Affiliation(s)
- S. Nazarian
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - H.F Koo
- Department of Surgery, Royal Free London NHS Foundation Trust, London, UK
| | - E. Carrington
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - A. Darzi
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - N. Patel
- Department of Surgery and Cancer, Imperial College London, London, UK
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Eysenbach G, Liu SHK, Leung K, Wu JT, Zauber AG, Leung WK. The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study. J Med Internet Res 2023; 25:e42665. [PMID: 36763451 PMCID: PMC9960036 DOI: 10.2196/42665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals. OBJECTIVE The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients. METHODS We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined. RESULTS A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected. CONCLUSIONS Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.
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Affiliation(s)
| | - Sze Hang Kevin Liu
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Wai Keung Leung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
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Koh FH, Ladlad J, Teo EK, Lin CL, Foo FJ. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc 2023; 37:165-171. [PMID: 35882667 PMCID: PMC9321269 DOI: 10.1007/s00464-022-09470-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/10/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps. METHODS A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy. RESULTS A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement. CONCLUSIONS Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.
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Affiliation(s)
- Frederick H. Koh
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | - Jasmine Ladlad
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | | | - Eng-Kiong Teo
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Cui-Li Lin
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Fung-Joon Foo
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore ,grid.508163.90000 0004 7665 4668Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, Singapore, Singapore
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Spadaccini M, Hassan C, Alfarone L, Da Rio L, Maselli R, Carrara S, Galtieri PA, Pellegatta G, Fugazza A, Koleth G, Emmanuel J, Anderloni A, Mori Y, Wallace MB, Sharma P, Repici A. Comparing the number and relevance of false activations between 2 artificial intelligence computer-aided detection systems: the NOISE study. Gastrointest Endosc 2022; 95:975-981.e1. [PMID: 34995639 DOI: 10.1016/j.gie.2021.12.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Artificial intelligence has been shown to be effective in polyp detection, and multiple computer-aided detection (CADe) systems have been developed. False-positive (FP) activation emerged as a possible way to benchmark CADe performance in clinical practice. The aim of this study was to validate a previously developed classification of FPs comparing the performances of different brands of approved CADe systems. METHODS We compared 2 different consecutive video libraries (40 video per arm) collected at Humanitas Research Hospital with 2 different CADe system brands (CADe A and CADe B). For each video, the number of CADe false activations, cause, and time spent by the endoscopist to examine the area erroneously highlighted were reported. The FP activations were classified according to the previously developed classification of FPs (the NOISE classification) according to their cause and relevance. RESULTS In CADe A 1021 FP activations were registered across the 40 videos (25.5 ± 12.2 FPs per colonoscopy), whereas in CADe B 1028 were identified (25.7 ± 13.2 FPs per colonoscopy; P = .53). Among them, 22.9 ± 9.9 (89.8% in CADe A) and 22.1 ± 10.0 (86.0% in CADe B) were because of artifacts from the bowel wall. Conversely, 2.6 ± 1.9 (10.2% in CADe A) and 3.5 ± 2.1 (14% in CADe B) were caused by bowel content (P = .45). Within CADe A each false activation required .2 ± .9 seconds, with 1.6 ± 1.0 FPs (6.3%) requiring additional time for endoscopic assessment. Comparable results were reported within CADe B with .2 ± .8 seconds spent per false activation and 1.8 ± 1.2 FPs per colonoscopy requiring additional inspection. CONCLUSIONS The use of a standardized nomenclature provided comparable results with either of the 2 recently approved CADe systems. (Clinical trial registration number: NCT04399590.).
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Ludovico Alfarone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Leonardo Da Rio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
| | - Silvia Carrara
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | | | - Gaia Pellegatta
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Alessandro Fugazza
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Glenn Koleth
- Department of Gastroenterology and Hepatology, Hospital Selayang, Selangor, Malaysia
| | - James Emmanuel
- Department of Gastroenterology and Hepatology, Queen Elizabeth Hospital, Sabah, Malaysia
| | - Andrea Anderloni
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Michael B Wallace
- Endoscopy Unit, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, USA
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy
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Zhang H. A Review of Convolutional Neural Network Development in Computer Vision. EAI ENDORSED TRANSACTIONS ON INTERNET OF THINGS 2022. [DOI: 10.4108/eetiot.v7i28.445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Convolutional neural networks have made admirable progress in computer vision. As a fast-growing computer field, CNNs are one of the classical and widely used network structures. The Internet of Things (IoT) has gotten a lot of attention in recent years. This has directly led to the vigorous development of AI technology, such as the intelligent luggage security inspection system developed by the IoT, intelligent fire alarm system, driverless car, drone technology, and other cutting-edge directions. This paper first outlines the structure of CNNs, including the convolutional layer, the downsampling layer, and the fully connected layer, all of which play an important role. Then some different modules of classical networks are described, and these modules are rapidly driving the development of CNNs. And then the current state of CNNs research in image classification, object segmentation, and object detection is discussed.
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Schrader C, Wallstabe I, Schiefke I. Künstliche Intelligenz in der Vorsorgekoloskopie. COLOPROCTOLOGY 2022. [DOI: 10.1007/s00053-022-00593-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Soons E, Rath T, Hazewinkel Y, van Dop WA, Esposito D, Testoni PA, Siersema PD. Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study. Int J Colorectal Dis 2022; 37:2219-2228. [PMID: 36163514 PMCID: PMC9560918 DOI: 10.1007/s00384-022-04258-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/18/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND AIMS Colonoscopy aims to early detect and remove precancerous colorectal polyps, thereby preventing development of colorectal cancer (CRC). Recently, computer-aided detection (CADe) systems have been developed to assist endoscopists in polyp detection during colonoscopy. The aim of this study was to investigate feasibility and safety of a novel CADe system during real-time colonoscopy in three European tertiary referral centers. METHODS Ninety patients undergoing colonoscopy assisted by a real-time CADe system (DISCOVERY; Pentax Medical, Tokyo, Japan) were prospectively included. The CADe system was turned on only at withdrawal, and its output was displayed on secondary monitor. To study feasibility, inspection time, polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion (SSL) detection rate (SDR), and the number of false positives were recorded. To study safety, (severe) adverse events ((S)AEs) were collected. Additionally, user friendliness was rated from 1 (worst) to 10 (best) by endoscopists. RESULTS Mean inspection time was 10.8 ± 4.3 min, while PDR was 55.6%, ADR 28.9%, and SDR 11.1%. The CADe system users estimated that < 20 false positives occurred in 81 colonoscopy procedures (90%). No (S)AEs related to the CADe system were observed during the 30-day follow-up period. User friendliness was rated as good, with a median score of 8/10. CONCLUSION Colonoscopy with this novel CADe system in a real-time setting was feasible and safe. Although PDR and SDR were high compared to previous studies with other CADe systems, future randomized controlled trials are needed to confirm these detection rates. The high SDR is of particular interest since interval CRC has been suggested to develop frequently through the serrated neoplasia pathway. CLINICAL TRIAL REGISTRATION The study was registered in the Dutch Trial Register (reference number: NL8788).
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Affiliation(s)
- E. Soons
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - T. Rath
- Department of Internal Medicine 1, Division of Gastroenterology, Friedrich-Alexander-University, Ludwig Demling Endoscopy Center of Excellence, Erlangen Nuernberg, Germany
| | - Y. Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - W. A. van Dop
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - D. Esposito
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. A. Testoni
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. D. Siersema
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:79-88. [DOI: 10.37126/aige.v2.i3.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.
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Affiliation(s)
- Niel Shah
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Abhilasha Jyala
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Harish Patel
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Jasbir Makker
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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