Minireviews
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Jun 28, 2021; 2(3): 79-88
Published online Jun 28, 2021. doi: 10.37126/aige.v2.i3.79
Utility of artificial intelligence in colonoscopy
Niel Shah, Abhilasha Jyala, Harish Patel, Jasbir Makker
Niel Shah, Abhilasha Jyala, Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
Harish Patel, Jasbir Makker, Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
Author contributions: Patel H conceived with an idea about this work and approved the final manuscript; Shah N, Jyala M and Makker J were responsible for reviewing of the literatures and studies, drafting the outline and main manuscript; Patel H and Makker J were responsible for critically reviewing the manuscript for intellectual content, reviewing of the literatures and editing of the main manuscript.
Conflict-of-interest statement: Authors have nothing to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Harish Patel, MD, Attending Doctor, Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, 1650 Grand Concourse , Bronx, NY 10457, United States. hpatel@bronxcare.org
Received: June 2, 2021
Peer-review started: June 2, 2021
First decision: June 18, 2021
Revised: June 20, 2021
Accepted: June 28, 2021
Article in press: June 28, 2021
Published online: June 28, 2021
Processing time: 34 Days and 7.8 Hours
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.

Keywords: Artificial intelligence; Colonoscopy; Colorectal cancer; Inflammatory bowel disease; Adenoma detection rate; Adenoma

Core Tip: Artificial intelligence (AI) pertains to performance of intelligent tasks like human beings by computer-controlled machines. Machine learning, one of the most important and fundamental principles of AI, essentially means automatically using the available data to learn and make decisions without human intervention. AI based detection models have been developed for polyp detection and to differentiate malignant from nonmalignant lesions. It has been also utilized to analyze endoscopic images for inflammatory bowel disease diagnosis, grading its severity and predicting treatment response.