Huang Yigeng, Li Suwen, Rubab Syeda Sadia, Bao Junjun, Hu Cui, Hong Jianglong, Ren Xiaofei, Liu Xiaochang, Zhang Lixiang, Huang Jian, Gan Huizhong, Zhou Xiaolan, Cao Jie, Fang Dong, Shi Zhenwang, Wang Huanqin, Mei Qiao
State Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
University of Science and Technology of China, Hefei, 230026, China.
Sci Rep. 2025 Apr 28;15(1):14927. doi: 10.1038/s41598-025-99725-y.
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.
结肠镜检查尖端的黏膜接触会导致视野变红，施加更大压力可能会导致穿孔。目前仍缺乏针对视野变红的定量分析方法。我们旨在开发一种基于人工智能（AI）的系统，以评估结肠镜检查插管过程中的视野变红情况。我们使用所提出的基于半监督人工智能的系统，对34位结肠镜检查医师进行的479例结肠镜检查进行了分析。我们比较了新手、中级和经验丰富的结肠镜检查医师基于人工智能的避免视野变红得分。在根据专家评定的基于直接观察操作或技能（DOPS）的尖端控制评估结果分层的组之间，比较了基于人工智能的平均避免视野变红得分。新手、中级和经验丰富的组之间，实际视野变红的百分比（p < 0.001）和基于人工智能的避免视野变红得分（p < 0.001）均存在显著差异。在基于DOPS的尖端控制评估中得分较高的结肠镜检查医师，在基于人工智能的避免视野变红技能评估中也表现更好。基于人工智能的避免视野变红得分与实际盲肠插管时间和实际视野变红百分比呈负相关。避免视野变红得分的反馈可能有助于提醒内镜医师以有效且安全的方式进行结肠镜检查。该系统可作为结肠镜检查培训的辅助工具。
