Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 21, 2020; 26(35): 5248-5255
Published online Sep 21, 2020. doi: 10.3748/wjg.v26.i35.5248
Is artificial intelligence the final answer to missed polyps in colonoscopy?
Thomas K L Lui, Wai K Leung
Thomas K L Lui, Wai K Leung, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China
Author contributions: Lui TKL contributed to drafting of manuscript; Leung WK contributed to critical review of manuscript.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript.
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:
Corresponding author: Wai K Leung, MD, Professor, Department of Medicine, Queen Mary Hospital, University of Hong Kong, 102 Pokfulam Road, Hong Kong, China.
Received: May 28, 2020
Peer-review started: May 28, 2020
First decision: June 18, 2020
Revised: June 30, 2020
Accepted: August 26, 2020
Article in press: August 26, 2020
Published online: September 21, 2020

Lesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.

Keywords: Artificial intelligence, Adenoma, Colonoscopy, Colorectal cancer, Polyps

Core Tip: This review highlights the results of recent studies on the use of artificial intelligence for the detection of colorectal polyps and its role in reducing missed lesions during colonoscopy.