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功能磁共振成像用于偏头痛分类的可解释人工智能分析:定量研究

Guohao Li¹, Hao Yang¹, Li He¹

¹West China Hospital, Sichuan University, Chengdu, China.

JMIR medical informatics

|September 3, 2025

概括

在fMRI数据中,可解释AI (XAI) 与区域功能连接强度 (RFCS) 结合,在偏头痛分类中获得了超过98%的准确性. XAI发现了前骨和后骨等关键大脑区域, 有助于了解偏头痛的进展.

科学领域:

神经科学
人工智能
医学成像

背景情况:

深度学习模型对诊断偏头痛等神经精神疾病有希望, 但缺乏解释性, 阻碍了临床使用.
在
黑盒子
这些模型的性质阻碍了生物标志物发现和个性化治疗策略.

研究的目的:

评估可解释的人工智能 (XAI) 技术与功能磁共振成像 (fMRI) 指标相结合,用于偏头痛分类.
确定人工智能模型和fMRI指标的最佳配对,以提高诊断准确度.
通过确定与偏头痛相关的大脑区分来评估XAI在临床环境中的潜力.

主要方法:

对64名参与者 (偏头痛患者和健康对照组) 的休息状态fMRI数据的分析.
三种fMRI指标的提取和分类:低频波动幅度,区域均性和区域功能连接强度 (RFCS).
使用深度学习模型 (GoogleNet,ResNet18,Vision Transformer) 和传统的机器学习方法 (SVM,随机森林) 来进行分类,XAI生成激活热图.

主要成果:

使用RFCS指标的GoogleNet模型获得了最高的分类准确度 (> 98.44%) 和AUC值为0.99.
与低频波动幅度相比,RFCS指标的分类精度提高了约8%.
通过XAI生成的热图显示前骨和后骨是偏头痛中最有区别的脑部区域.

结论:

XAI与fMRI脑区特征相结合, 提供了对偏头痛进展的视觉解释.
通过XAI了解人工智能决策过程有很大的潜力改善临床偏头痛诊断.
这种方法有望提高诊断准确性和开发新的偏头痛诊断技术.

关键词:

美国卷积神经网络可解释的人工智能梯度加权类激活映射偏头痛个性化诊断

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