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Multicenter Study
. 2023 Feb;21(2):337-346.e3.
doi: 10.1016/j.cgh.2022.07.006. Epub 2022 Jul 19.

Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial

Hong Xu  1 , Raymond S Y Tang  2 , Thomas Y T Lam  3 , Guijun Zhao  4 , James Y W Lau  5 , Yunpeng Liu  6 , Qi Wu  7 , Long Rong  8 , Weiran Xu  1 , Xue Li  4 , Sunny H Wong  9 , Shuntian Cai  6 , Jing Wang  7 , Guanyi Liu  8 , Tantan Ma  1 , Xiong Liang  4 , Joyce W Y Mak  2 , Hongzhi Xu  6 , Peng Yuan  7 , Tingting Cao  1 , Fudong Li  1 , Zhenshi Ye  6 , Zhang Shutian  10 , Joseph J Y Sung  11
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Free article
Multicenter Study

Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial

Hong Xu et al. Clin Gastroenterol Hepatol. 2023 Feb.
Free article

Abstract

Background and aims: Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.

Methods: This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45-75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist's experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393).

Results: From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group.

Conclusions: In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548).

Keywords: Artificial Intelligence; Colonoscopy; Colorectal Cancer Screening.

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