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一个可解释的机器学习框架,具有基于数据的成像生物标志物,用于诊断和预测阿尔茨海默病

Wenjie Kang¹, Bo Li², Lize C Jiskoot³

¹Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|February 8, 2026

概括

这项研究引入了一个可解释的机器学习框架,用于阿尔茨海默病 (AD) 诊断. 它将深度学习与可解释的模型相结合,以实现高精度,同时确保临床使用的透明决策.

科学领域:

神经科学是一个神经科学.
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:

机器学习显示出使用临床和成像数据对阿尔茨海默病 (AD) 诊断的前景.
当前的深度学习模型往往缺乏透明度,阻碍了临床采用.
可解释增强机器 (EBM) 提供可解释性,但通常仅限于低维数据.

研究的目的:

开发一个可解释的机器学习框架,用于AD诊断和预测.
为了透明度,将卷积神经网络 (CNN) 与EBM集成用于特征提取.
为了使诊断预测能够在团体层面和个人层面进行解释.

主要方法:

提出了一个新的框架,将基于CNN的特征提取与EBM结合起来.
将框架应用于阿尔茨海默病神经成像计划 (ADNI) 队列进行验证.
在独立队列上进行外部验证,以评估可通用性.

主要成果:

在ADNI队列中实现了0.969的AD vs.对照分类的AUC.
预测的轻度认知障碍 (MCI) 转换在ADNI队列上的AUC为0.750.
外部验证显示AUC为0.871 (AD与主观认知衰退) 和0.666 (MCI转换).

结论:

开发的框架实现了与最先进的黑子模型相提并论的性能.
提供透明的决策,这对于AI在AD诊断中的临床转化至关重要.
识别了关键的成像生物标志物,这些生物标志物有助于在多个层面上进行诊断预测.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.卷积神经网络是一个卷积神经网络.可以解释的提升机器磁共振成像技术磁共振成像技术封闭地图是一个封闭地图.

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