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自动分类神经肌肉疾病使用大腿肌肉MRI与模型解释

Lotte Huysmans^1,2, Louise Iterbeke³, Bram De Wel^3,4

¹Medical Imaging Research Centre, University Hospitals Leuven, Leuven, Belgium.

Journal of cachexia, sarcopenia and muscle

|October 11, 2025

概括

这项研究开发了一种使用MRI扫描的自动化方法,以准确诊断四种类型的神经肌肉疾病 (NMD) 和健康对照,达到89%的准确性. 该方法提供了与已知的疾病模式相关的可解释结果.

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:

神经肌肉疾病 (NMDs) 的诊断依赖于临床评估,肌电图,神经传导研究,血液测试,肌肉活检和遗传测试.
肌肉MRI可视化受影响的区域,识别脂肪替代,缩和,有不同的模式帮助诊断.
当前的诊断方法可能具有侵入性和耗时性,突出了对先进的非侵入性技术的需求.

研究的目的:

开发一种自动化的方法来分类NMD使用上腿症状MRI扫描.
为分类模型的决定提供可解释的解释.
根据已建立的诊断标准和文献验证模型的性能.

主要方法:

使用了来自四个NMD (LGMDR12,BMD,DM1,CMT1A) 和健康对照的109个迪克森上腿MRI扫描.
训练了一个U-Net模型用于肌肉细分,计算脂肪分数,并将其作为随机森林分类器的输入.
采用了SHapley添加式解释 (SHAP) 来解释模型推理,并将发现与医学文献进行了比较.

主要成果:

在区分NMD类别和健康对照中获得了89%的整体准确性.
所有类的曲线下面积 (AUC) 值都显示为很高 (0.972-0.997).
证实模型性能无论细分精度或特征提取方法如何,都是一致的,SHAP解释与已知的肌肉参与模式保持一致.

结论:

一种完全自动化的方法有效地区分了四个NMD和使用MRI数据的健康对照.
开发的方法为NMD诊断提供了一个准确和可解释的工具.
这种自动化方法有望提高NMD诊断的效率和准确性.

关键词:

简介:SHAP的解释疾病分类疾病分类.神经肌肉疾病神经肌肉疾病定量的MRI是指MRI的数量.

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