机器学习模型用于评估梅奥内镜评分在性结肠炎试验终点:一个系统性审查
David T Rubin1, Walter Reinisch2, Neeraj Narula3
1Inflammatory Bowel Disease Center, University of Chicago Medicine, Chicago, IL, United States.
机器学习模型在从性结肠炎 (UC) 视频中评估梅奥内镜评分 (MES) 中表现出强的表现,为临床试验提供了可重复的方法. 这项技术可以将内镜严重性评估标准化.
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:
- 梅奥内镜评分 (MES) 对于性结肠炎 (UC) 临床试验至关重要,但受到主观人类评估的影响.
- 机器学习 (ML) 为标准化MES评估和提高可重现性提供了一个潜在的解决方案.
- 了解ML模型的性能是UC研究中更广泛应用的关键.
研究的目的:
- 系统地审查ML模型的培训和测试,以预测UC患者内镜视频中的MES.
- 评估基于ML的MES预测模型的性能特征.
主要方法:
- 在PubMed/MEDLINE,EMBASE和Web of Science的系统文献搜索到2024年12月31日.
- 包括在UC内镜视频上进行自动MES分级的ML模型培训或测试的研究.
- 参考检查和谷歌搜索补充了主要的数据库搜索.
主要成果:
- 七项研究符合纳入标准;五项报告了模型性能.
- 预测顺序MES等级 (0-3) 的准确性在56.8%至83.3%之间.
- 预测二分化MES (改善/缓解定义) 的准确性在84%至95.5%之间.
结论:
- ML模型在UC内镜视频中展示了MES评估的强大性能.
- 这项技术提供了一种标准化和可重复的方法来测量内镜严重性.
- 需要进一步的研究来评估基于ML的MES评估对临床试验结果的影响.
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