Artificial intelligence alert system based on intraluminal view for colonoscopy intubation

作者：Yigeng Huang, Suwen Li, Syeda Sadia Rubab, Junjun Bao, Hu Cui, Jianglong Hong, Xiaofei Ren, Xiaochang Liu, Lixiang Zhang, Jian Huang, Huizhong Gan, Xiaolan Zhou, Jie Cao, Fang Dong, Zhenwang Shi, Huanqin Wang, Qiao Mei · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-99725-y · 被引用次数：1 · 研究领域：Colorectal Cancer Screening and Detection

Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative analysis methods for red-out views. We aimed to develop an artificial intelligence (AI)-based system to assess red-out views during intubation in colonoscopy. Altogether, 479 colonoscopies performed by 34 colonoscopists were analysed using the proposed semi-supervised AI-based system. We compared the AI-based red-out avoiding scores among novice, intermediate, and experienced colonoscopists. The mean AI-based red-out avoiding scores were compared among groups stratified by expert-rated direct observation of procedure or skill (DOPS)-based tip control assessment results. Both the percentage of actual red-out views (p < 0.001) and AI-based red-out avoiding scores (p < 0.001) were significantly different among the novice, intermediate, and experienced groups. Colonoscopists who scored better on the DOPS-based tip control assessment also performed better on the AI-based red-out avoiding skill assessment. AI-based red-out avoiding score was negatively correlated with actual caecal intubation time and actual red-out percentage. Feedback of red-out avoiding score may help remind endoscopists to perform colonoscopy in an effective and safe manner. This system can be used as an auxiliary tool for colonoscopy training.