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Artif Intell Gastrointest Endosc. Dec 28, 2021; 2(6): 211-219
Published online Dec 28, 2021. doi: 10.37126/aige.v2.i6.211
Artificial intelligence in polyp detection - where are we and where are we headed?
Kristen E Dougherty, Vatche J Melkonian, Grace A Montenegro
Kristen E Dougherty, Vatche J Melkonian, Grace A Montenegro, Department of Surgery, St. Louis University Hospital, Saint Louis, MO 63110, United States
Author contributions: Dougherty KE and Melkonian VJ and Montenegro GA contributed equally to this work; Dougherty KE and Melkonian VJ and Montenegro GA performed writing, review and editing of the manuscript.
Conflict-of-interest statement: None of the authors have disclosures.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Grace A Montenegro, FACS, MD, MS, Assistant Professor, Surgeon, Department of Surgery, St. Louis University Hospital, 1008 S. Spring Avenue, SLUCare Academic Pavilion, Saint Louis, MO 63110, United States. grace.montenegro@health.slu.edu
Received: April 27, 2021
Peer-review started: April 28, 2021
First decision: May 19, 2021
Revised: July 2, 2021
Accepted: November 18, 2021
Article in press: November 18, 2021
Published online: December 28, 2021
Processing time: 244 Days and 12.1 Hours
Abstract

The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer. Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks. Computer aided detection (CADe) utilizing neural networks allows real time detection of polyps and adenomas. Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies. We review the most recent prospective randomized controlled trials here. These randomized control trials, both non-blinded and blinded, demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes. Increase of polyps and adenomas detected were mainly small and sessile in nature. CADe systems were found to be safe with little added time to the overall procedure. Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy. Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions, non-blinded arms, and question of external validity.

Keywords: Neural networks; Computer aided detection; Artificial intelligence in colonoscopy and polyp detection; Artificial intelligence in adenoma detection

Core Tip: Use of computer aided detection (CADe) in colonoscopy has been shown to increase polyp and adenoma detection rates compared to standard high-definition colonoscopy with little added procedure time. Additionally, CADe have been built to increase quality of screening colonoscopy. These advantages and features have been demonstrated in blinded and non-blinded randomized controlled trials.