结肠囊内镜肠道准备评分的观察者间和观察者内部变化:人工智能辅助评估可行性研究的影响
Ian Io Lei1,2, Daniel R Gaya3, Alexander Robertson4
1Institute of Precision Diagnostics & Translational Medicine, University Hospital of Coventry and Warwickshire, Clifford Bridge Rd, Coventry CV2 2DX, UK.
人工智能辅助结肠囊内镜 (CCE) 清洁得分没有改善观察者之间的一致性,并且可能会降低经验较少的读者的一致性. 为了在CCE诊断中广泛实施AI,需要进一步精细化.
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:
- 结肠囊内镜 (CCE) 的采用正在增加,作为一种非侵入性下部肠道肠道检查方法.
- 不充分的肠道准备是CCE的一个主要限制,通常需要转换到结肠镜.
- 手动评估肠道清洁性受主观性和观察者之间的高变化影响.
研究的目的:
- 通过使用莱顿-雷克斯和CC-CLEAR等级来评估CCE肠道清洁评估的观察者间一致性.
- 为了确定人工智能辅助评分对观察者间和观察者内部协议的影响,与手动评估相比.
- 评估基于TransUNet的细分模型,用于CCE肠道准备的定制补丁损失功能.
主要方法:
- 九位具有不同CCE经验的读者使用莱顿-雷克斯和CC-CLEAR等级评分了25个CCE视频.
- 读者在8周的洗期后使用人工智能辅助的CC-CLEAR分数重新评估视频.
- 使用ICC和Fleiss' Kappa评估了观察者间的变异性;使用Cohen's Kappa和配对t测试评估了观察者内部的变异性.
主要成果:
- CC-CLEAR显示了相当的优秀协议 (ICC=0.90),优于莱顿雷克斯 (ICC=0.55).
- 人工智能辅助的CC-CLEAR取得了中等的一致性 (ICC=0.69),在不那么有经验的读者中表现较差 (ICC=0.56).
- 人工智能辅助的分数明显低于手动阅读,可能会增加结肠镜转换率.
结论:
- 人工智能辅助的评分没有增强观察者之间的一致性,并且可能会降低经验较少的读者之间的一致性.
- 目前的AI工具的价值仅限于经验丰富的CCE读者.
- 为了有效实施CCE,需要进一步改进人工智能,包括空间分析.
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