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使用大脑皮层复杂性的机器学习模型来诊断阿尔茨海默病

Shaofan Jiang^1,2, Siyu Yang^3,4,5, Kaiji Deng¹

¹Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.

Frontiers in aging neuroscience

|October 25, 2024

概括

使用碎形维度 (FD) 的机器学习模型显示出用于诊断阿尔茨海默病 (AD) 的前景. MoCA + FD模型展示了最高的预测效率,表明AD的潜在非侵入性诊断工具.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
人工智能的人工智能

背景情况:

阿尔茨海默病 (AD) 诊断依赖于复杂的评估.
用碎形维度 (FD) 测量的皮层复杂性是AD的潜在生物标志物.
机器学习模型 (MLM) 为疾病诊断提供了新的方法.

研究的目的:

开发和验证使用皮层复杂性 (FD) 诊断AD的MLM.
与其他临床和生物标志物相比,评估基于FD的MLM的诊断性能.
评估MLM在AD诊断中的临床实用性.

主要方法:

从ADNI的296名正常认知 (NC) 和182名AD参与者中,从30个显著改变的皮质区域开发了使用FD的MLM.
内部和外部使用机构队列 (n=66) 验证的模型.
使用接收器操作特征曲线 (AUC) 和决策曲线分析评估模型性能.

主要成果:

FD模型在三个队列中预测AD的准确性很好 (AUC:0.842,0.808,0.803).
在所有队列中,MoCA + FD模型实现了最高的预测效率 (AUC:0.951,0.931,0.955).
摩卡+FD模型显示出最大的临床净益.

结论:

基于FD的MLM显示了AD的良好诊断性能.
摩卡+FD模型是AD的高效预测器.
这种方法为AD诊断提供了潜在的非侵入性方法.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.蒙特利尔的认知评估亚脂蛋白 E 是一种非脂蛋白 E.机器学习是机器学习.磁共振成像技术的使用

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