Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China.
Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China.
Clin Gastroenterol Hepatol. 2023 Feb;21(2):337-346.e3. doi: 10.1016/j.cgh.2022.07.006. Epub 2022 Jul 19.
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.
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).
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.
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).
人工智能（AI）辅助结肠镜检查可提高结肠镜检查中息肉的检出率和特征描述能力。然而，缺乏来自无症状人群的大规模多中心随机对照试验（RCT）的数据。
这项多中心 RCT 旨在比较 AI 辅助结肠镜检查与常规结肠镜检查在无症状人群中对腺瘤的检出率。在香港、吉林、内蒙古、厦门和北京的 6 家转诊中心，招募年龄在 45-75 岁之间、通过直接结肠镜检查或粪便免疫化学试验进行结直肠癌筛查的无症状受试者。在 AI 辅助结肠镜检查中，使用实时通知内镜系统同一监视器上的 AI 息肉检测系统（Eagle-Eye）。主要结局是总体腺瘤检出率（ADR）。次要结局是每例结肠镜检查的平均腺瘤数、根据内镜医生经验的 ADR 以及结肠镜检查退出时间。本研究获得机构审查委员会批准（CRE-2019.393）。
2019 年 11 月至 2021 年 8 月，3059 名受试者被随机分配至 AI 辅助结肠镜组（n=1519）和常规结肠镜组（n=1540）。两组的基线特征和肠道准备质量相似。总体 ADR（39.9%比 32.4%；P<0.001）、高级 ADR（6.6%比 4.9%；P=0.041）、专家（42.3%比 32.8%；P<0.001）和非专家内镜医生（37.5%比 32.1%；P=0.023）的 ADR 以及每例结肠镜检查的腺瘤数（0.59±0.97 比 0.45±0.81；P<0.001）均显著升高。AI 辅助结肠镜组的中位退出时间（8.3 分钟比 7.8 分钟；P=0.004）略长。
在这项无症状患者的多中心 RCT 中，AI 辅助结肠镜检查提高了总体 ADR、高级 ADR 以及专家和非专家内镜医生的 ADR。（临床试验.gov，编号：NCT04422548）。
