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可解释的机器学习模型用于在第一发精神病中描述基于磁感应的生物标志物

Pamela Franco¹, Cristian Montalba², Raúl Caulier-Cisterna³

¹Energy Transformation Center, Faculty of Engineering, Universidad Andrés Bello, Santiago, Chile.

Computer methods and programs in biomedicine

|September 10, 2025

概括

这项研究使用了定量敏感性映射 (QSM) 和机器学习来预测精神病和抗精神病反应在第一发作精神病 (FEP) 患者. 这些发现突出了QSM.

科学领域:

神经成像是一种神经成像.
放射学放射学是指放射学
精神病学是一个精神病学.

背景情况:

在精神病患者中观察到深层大脑核神经化学的变化.
在皮下区域的多巴胺功能障碍与铁度波动有关.
定量敏感度映射 (QSM) 测量铁度,用于识别潜在的多巴胺功能障碍.

研究的目的:

采用随机森林算法来预测第一期精神病 (FEP) 的易感特征.
使用QSM衍生的特征预测FEP患者的抗精神病治疗反应.
使用沙普利附加性解释 (SHAP) 值来解释特征.

主要方法:

从61名健康志愿者 (HV) 和76名FEP患者获得3D多回声梯度回声 (GRE) 和T1加权的GRE MRI数据.
在22个细分感兴趣的地区重建和平均QSM和R2*.
应用顺序前选择和随机森林用于预测,与SHAP用于特征解释和对关联模式的等级聚类.

主要成果:

使用四个特征对HV与FEP患者 (76.48%) 和耐治疗精神分裂症 (TRS) 与治疗反应性精神分裂症 (RS) 患者 (76.43%) 的准确分类.
SHAP分析确定了选定的磁感应特征之间的四大非线性关系.
层次聚类揭示了两个不同的组的相关特征.

结论:

使用QSM特征的机器学习模型可以预测精神病和治疗反应.
对治疗反应的早期预测对于为FEP患者开发量身定制的干预措施至关重要,特别是那些有治疗耐药性的患者.

关键词:

生物标志物,用于医学成像的人工智能.可以解释的机器学习磁共振成像技术磁共振成像技术定量敏感性映射测绘定量敏感性映射测绘

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