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Updated: Jan 23, 2026

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使用卷积神经网络在肠镜中对小肠和结肠清洁的自动评估
Pedro Marílio Cardoso1,2, Miguel Mascarenhas1,2, Miguel Martins1,2
1Gastroenterology, Centro Hospitalar Universitário de São João, Porto, Portugal.
Endoscopy international open
|January 22, 2026
概括
一个新的人工智能模型准确地评估了设备辅助肠镜 (DAE) 期间的肠道清洁度. 这种卷积神经网络 (CNN) 提供客观的泛内镜评估,改善了胃肠道手术的诊断和治疗成功.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 设备辅助肠镜 (DAE) 需要最佳的肠道准备,以获得诊断和治疗成功.
- 目前评估肠道准备的方法是主观的,范围有限.
- 需要在DAE中对肠道清洁性的客观,自动评估.
研究的目的:
- 开发和评估一个卷积神经网络 (CNN),用于在DAE期间自动评估肠道清洁.
- 为了解决小肠和结肠清洁评估的问题.
- 为提供准备品质的客观,全内镜测量.
主要方法:
- 对28个DAE程序进行了回顾性分析.
- 一个CNN的开发训练了小肠和结肠部分的88,623张图像.
- 评估CNN的表现与专家的共识,使用灵敏度,特异性,准确性和AUC-ROC.
主要成果:
- 美国有线电视新闻 (CNN) 获得了极好的 (90.6%),令人满意的 (92.7%) 和不令人满意的 (96.8%) 肠道清洗的高准确性.
- 在ROC曲线下的面积 (AUC-ROC) 值在0.95到0.96.9之间.
- 该模型在不同的清洁等级中表现出强的性能.
结论:
- 这项研究介绍了第一个CNN对DAE.的全内镜肠道清洁性评估.
- 开发的CNN提供了高精度和实时临床应用的潜力.
- 这项技术代表了标准化清洁性评估和提高内镜质量的重要一步.
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