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<p>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 (<i>p</i> &lt; 0.001) and AI-based red-out avoiding scores (<i>p</i> &lt; 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.</p>

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Artificial intelligence alert system based on intraluminal view for colonoscopy intubation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Yigeng Huang,
  • Suwen Li,
  • Syeda Sadia Rubab,
  • Junjun Bao,
  • Cui Hu,
  • Jianglong Hong,
  • Xiaofei Ren,
  • Xiaochang Liu,
  • Lixiang Zhang,
  • Jian Huang,
  • Huizhong Gan,
  • Xiaolan Zhou,
  • Jie Cao,
  • Dong Fang,
  • Zhenwang Shi,
  • Huanqin Wang,
  • Qiao Mei

摘要

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.