在临床数据仓库中评估基于MRI的机器学习方法,用于计算机辅助的痴呆症诊断
Simona Bottani1, Ninon Burgos1, Aurélien Maire2
1Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France.
Medical image analysis
|July 31, 2023
概括
计算机辅助痴呆症诊断算法在研究数据上显示出高准确性,但在临床环境中失败. 像图像质量这样的混因素会产生"聪明的汉斯"效应,导致现实世界表现差.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 使用脑MRI进行痴呆的计算机辅助诊断 (CAD) 系统显示出有前途.
- 在研究数据集上报告的高准确性与未经证实的临床实用性形成鲜明对比.
研究的目的:
- 在真实世界的临床数据上评估CAD系统的性能.
- 将临床性能与研究数据集进行比较.
- 调查在常规临床实践中影响CAD准确性的偏见.
主要方法:
- 从一个多医院数据仓库 (大巴黎地区) 提取临床数据.
- 利用了来自阿尔茨海默病神经成像计划的研究数据.
- 使用ICD-10诊断码识别患者队列.
- 分析了图像质量和对比剂注射对性能的影响.
主要成果:
- 由于从图像质量和对比剂混器中学习快捷方式 (Clever Hans效应),CAD性能被显著高估 (平衡精度>17%).
- 在纠正偏差后,临床数据的表现非常差.
- 临床性能明显低于研究数据集.
结论:
- 目前用于痴呆症诊断的CAD系统在临床翻译方面面临重大挑战.
- 临床数据中的混变量可能导致误导性高性能估计.
- 需要进一步的研究来开发可靠的CAD系统,用于常规的痴呆症诊断.
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