☆ 4.7 Article

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

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2023)

Journal

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY

Volume 21, Issue 2, Pages -

Publisher

ELSEVIER SCIENCE INC

DOI: 10.1016/j.cgh.2022.07.006

Keywords

Artificial Intelligence; Colonoscopy; Colorectal Cancer Screening

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Abstract

This multicenter randomized controlled trial found that AI-assisted colonoscopy improves polyp detection and characterization in an asymptomatic population. AI-assisted colonoscopy can increase the adenoma detection rate and the detection ability of both expert and non-expert endoscopists.

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 notifica-tion 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 co-lonoscopy, 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 colo-noscopy (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 colonos-copy. 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).

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The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization

Edward Young, Louisa Edwards, Rajvinder Singh

Summary: Artificial intelligence (AI) systems have become increasingly available in the field of endoscopy, particularly in colonoscopy. This comprehensive review focuses on the application of AI in detecting and characterizing colorectal polyps, with the goal of improving the efficacy of colorectal cancer screening and prevention. AI-driven algorithms show potential in addressing the challenge of overlooked polyps and empowering gastroenterologists to accurately characterize polyps without extensive training in advanced mucosal imaging. By integrating AI technologies into routine colonoscopy, the detection and characterization of polyps can be advanced, potentially leading to more effective colorectal cancer screening and prevention.

CANCERS (2023)