使用人工智能开发的医学成像应用程序显示出高的内部有效性,但范围有限,缺乏外部验证
Jacob F Oeding1, Aaron J Krych2, Andrew D Pearle3
1Mayo Clinic Alix School of Medicine, Rochester, Minnesota, U.S.A.
概括
深度学习 (DL) 模型在体育医学中表现出色的诊断性能,但缺乏外部验证和临床适用性. 未来的研究应该专注于概括性和超越常规任务的推进,以获得更广泛的影响.
科学领域:
- 骨科运动医学 运动医学
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 深度学习 (DL) 是一种使用神经网络从媒体输入进行预测的AI领域.
- 运动医学中DL的采用正在迅速增加,需要对其应用和局限性进行审查.
研究的目的:
- 审查用于解释体育医学中DL应用的定义和概念.
- 为了确定DL模型改进的文献中的知识和翻译差距.
主要方法:
- 在2023年12月对体育医学DL进行了全面的文献搜索.
- 研究分析了联合焦点,病理学,成像模式,数据源,模型性能和外部验证.
- 一个五分级评价了DL模型的临床影响,从概念验证到实践改变.
主要成果:
- 最近发表的55项研究表明,DL模型主要用于分类 (84%) 的伤害,如ACL,旋转手套和半径撕裂.
- 磁共振成像 (62%) 是最常见的模式;诊断性能 (AUROC) 对眼 (0.75-0.99) 非常出色.
- 只有7%的研究对外部验证的模型进行了验证,平均临床影响评分为2/5,表明适用性有限.
结论:
- 骨科体育医学中的DL模型显示出高的内部有效性,但在临床部署时需要外部验证.
- 目前的DL模型由于专注于例行任务和狭窄的概念框架,具有较低的临床适用性,阻碍了现场进步.
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