一个人工智能引导的策略来减少肠道不良准备:一个多中心的随机对照研究
Antonio Z Gimeno-García1,2, Federica Benítez-Zafra1, Ignacio Redondo-Zaera3
1Servicio de Gastroenterología. Hospital Universitario de Canarias. Tenerife. Spain.
The American journal of gastroenterology
|January 20, 2026
概括
使用人工智能的新软件应用程序显著改善了结肠镜检查中的结肠清洁质量. 这种人工智能驱动的方法提高了肠道的准备,从而在门诊手术中获得更好的结果.
科学领域:
- 胃肠病学和内镜检查
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 高质量的肠道准备对于在结肠镜检查期间准确检测腺瘤至关重要.
- 目前用于评估肠道准备的标准方法可能是主观和不一致的.
- 需要新的技术解决方案来客观地提高结肠清洁质量.
研究的目的:
- 评估使用卷积神经网络 (CNN) 提高结肠清洁质量的软件应用程序的有效性.
- 为了比较软件驱动的肠道准备方法与标准护理在多中心随机对照试验.
- 评估基于人工智能对直肠排泄物图像的反是否可以改善结肠镜准备.
主要方法:
- 一个多中心随机对照试验,涉及774名患者.
- 患者被分配到标准护理或干预组,使用基于CNN的反的网络应用程序.
- 干预组提交了直肠排泄物的图像用于AI分析,并获得了关于准备充分性的指导.
主要成果:
- 干预组在整体肠道清洁质量上显示了统计学上显著的改善 (91%对84.2%,P=0.005).
- 在干预组的右结肠和左结肠两部分都观察到高级清洁.
- 每项方案分析证实,干预组的肠道清洁显著改善,包括实现出色的准备 (BBPS>7).
结论:
- 一个软件应用程序利用CNN对直肠废水图像的分析,显著提高了门诊患者的结肠清洁质量.
- 这种人工智能驱动的方法为优化结肠镜检查前的肠道准备提供了一个有前途的工具.
- 这项研究表明,将人工智能整合到患者指导的准备方案中的临床实用性.
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