Published online Dec 28, 2021. doi: 10.37126/aige.v2.i6.211
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
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.
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.