Yao Liwen, Li Xun, Wu Zhifeng, Wang Jing, Luo Chaijie, Chen Boru, Luo Renquan, Zhang Lihui, Zhang Chenxia, Tan Xia, Lu Zihua, Zhu Ci, Huang Yuan, Tan Tao, Liu Zhifeng, Li Ying, Li Shuyu, Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Digestive Endoscopy Center, Wuhan Eighth Hospital, Wuhan, China.
Gastrointest Endosc. 2024 Jan;99(1):91-99.e9. doi: 10.1016/j.gie.2023.07.044. Epub 2023 Aug 1.
The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts.
This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR).
A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]).
AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).
由人工智能（AI）辅助的新手进行结肠镜检查的有效性和安全性尚不清楚。本研究的目的是比较新手、AI辅助新手和专家的病变检测能力。
本多中心、随机、非劣效性串联研究于2022年5月1日至2022年11月11日在中国的3家医院进行。符合条件的患者被随机分为3组中的1组：CN组（对照新手组，由新手独立退镜）、AN组（AI辅助新手组，由新手在AI辅助下退镜）或CE组（对照专家组，由专家独立退镜）。参与者接受由AI辅助专家进行的重复结肠镜检查，以评估病变漏诊率并确保病变检测。主要结局是腺瘤漏诊率（AMR）。
共分析了685例符合条件的患者：CN组229例，AN组227例，CE组229例。AN组的AMR和息肉漏诊率均低于CN组（分别为18.82%对43.69%[P <.001]和21.23%对35.38%[P <.001]）。AN组和CE组的AMR和息肉漏诊率均满足非劣效性界值（18.82%对26.97%[P =.202]和21.23%对24.10%[P <.249]）。
AI辅助结肠镜检查降低了新手的AMR，使其不劣于专家。AI辅助结肠镜检查可提高新内镜医师的退镜技术。（临床试验注册号：NCT05323279。）
