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一个人工智能系统,用于在结肠镜撤回期间合格的粘膜观察时间

Wu-Jun Li^1,2,3,4, Peng Yan⁵, Muhan Ni⁵

¹Department of Gastroenterology, Jiangsu Provincial Gastrointestinal Medical Innovation Center, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China. liwujun@nju.edu.cn.

NPJ digital medicine

|November 18, 2025

概括

人工智能 (AI) 系统QAMaster在结肠镜检查期间自动计算合格的粘膜观察时间 (QMOT). 更高的QMOT显著增加了腺瘤检测率 (ADR),改善了结肠镜质量.

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:

结肠镜抽取时间对于腺瘤检测率 (ADR) 和降低结肠直肠癌风险至关重要.
在例行结肠镜检查中,手动量化合格的粘膜观察时间 (QMOT) 是一个挑战.
在结肠镜检查期间加强粘膜观察与改善的ADR有关.

研究的目的:

开发和验证一个人工智能 (AI) 系统,QAMaster,用于自动计算QMOT.
评估QMOT和ADR之间的相关性.
评估QAMaster作为改善结肠镜质量评估的工具的潜力.

主要方法:

使用两个人工智能模型开发QAMaster:模型I用于图像质量分析和模型II用于解剖学地标识别.
培训数据集包括57,235张来自64名患者的图像,用于模型I,以及7,712张来自3,013名患者的图像,用于模型II.
患者被分为高 (≥90年代) 和低 (<90年代) 的QMOT组来比较ADR.

主要成果:

模型I实现了0.980-0.991的AUC,而模型II实现了0.977-0.997的AUC,证明了高精度.
在一个由482名患者组成的队列中,高QMOT组的ADR (36.54%) 与低QMOT组 (19.94%) 相比显著更高.
在高QMOT组中检测腺瘤的调整几率比为2.02 (95% CI 1.23-3.33).

结论:

在结肠镜抽取期间,QAMaster准确计算QMOT.
较高的QMOT与ADR增加显著相关.
QAMaster提供了一个有希望的,自动化解决方案,用于评估结肠镜撤回质量,并可能改善患者的治疗结果.

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