Li Guohao, Yang Hao, He Li, Zeng Guojun
West China Hospital, Sichuan University, Chengdu, China.
Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
JMIR Med Inform. 2025 Sep 3;13:e72155. doi: 10.2196/72155.
Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accuracy of deep learning models, the interpretability of image classification models remains limited. Their black-box nature continues to pose a major obstacle in clinical applications, hindering biomarker discovery and personalized treatment.
This study aims to investigate explainable artificial intelligence (XAI) techniques combined with multiple functional magnetic resonance imaging (fMRI) indicators to (1) compare their efficacy in migraine classification, (2) identify optimal model-indicator pairings, and (3) evaluate XAI's potential in clinical diagnostics by localizing discriminative brain regions.
We analyzed resting-state fMRI data from 64 participants, including 21 (33%) patients with migraine without aura, 15 (23%) patients with migraine with aura, and 28 (44%) healthy controls. Three fMRI metrics-amplitude of low-frequency fluctuation, regional homogeneity, and regional functional connectivity strength (RFCS)-were extracted and classified using GoogleNet, ResNet18, and Vision Transformer. For comprehensive model comparison, conventional machine learning methods, including support vector machine and random forest, were also used as benchmarks. Model performance was evaluated through accuracy and area under the curve metrics, while activation heat maps were generated via gradient-weighted class activation mapping for convolutional neural networks and self-attention mechanisms for Vision Transformer.
The GoogleNet model combined with RFCS indicators achieved the best classification performance, with an accuracy of >98.44% and an area under the receiver operating characteristic curve of 0.99 for the test set. In addition, among the 3 indicators, the RFCS indicator improved accuracy by approximately 8% compared with the amplitude of low-frequency fluctuation. Brain activation heat maps generated by XAI technology revealed that the precuneus and cuneus were the most discriminative brain regions, with slight activation also observed in the frontal gyrus.
The use of XAI technology combined with brain region features provides visual explanations for the progression of migraine in patients. Understanding the decision-making process of the network has significant potential for clinical diagnosis of migraines, offering promising applications in enhancing diagnostic accuracy and aiding in the development of new diagnostic techniques.
深度学习在推进神经精神疾病（如偏头痛）的计算机辅助诊断方面已展现出巨大潜力，能够在个体层面实现针对患者的诊断。然而，尽管深度学习模型具有卓越的准确性，但其图像分类模型的可解释性仍然有限。它们的黑箱性质在临床应用中持续构成重大障碍，阻碍了生物标志物的发现和个性化治疗。
本研究旨在探究可解释人工智能（XAI）技术与多种功能磁共振成像（fMRI）指标相结合，以（1）比较它们在偏头痛分类中的功效，（2）确定最佳的模型 - 指标配对，以及（3）通过定位有鉴别力的脑区来评估XAI在临床诊断中的潜力。
我们分析了64名参与者的静息态fMRI数据，其中包括21名（33%）无先兆偏头痛患者、15名（23%）有先兆偏头痛患者和28名（44%）健康对照者。提取了三种fMRI指标——低频波动幅度、局部一致性和区域功能连接强度（RFCS），并使用谷歌网络（GoogleNet）、残差网络18（ResNet18）和视觉Transformer进行分类。为了进行全面的模型比较，还使用了包括支持向量机和随机森林在内的传统机器学习方法作为基准。通过准确率和曲线下面积指标评估模型性能，同时通过梯度加权类激活映射为卷积神经网络生成激活热图，并为视觉Transformer生成自注意力机制热图。
结合RFCS指标的GoogleNet模型实现了最佳分类性能，测试集的准确率>98.44%，受试者工作特征曲线下面积为0.99。此外，在这3个指标中，与低频波动幅度相比，RFCS指标使准确率提高了约8%。XAI技术生成的脑激活热图显示，楔前叶和楔叶是最具鉴别力的脑区，额叶回也观察到轻微激活。
将XAI技术与脑区特征相结合可为偏头痛患者病情进展提供可视化解释。了解网络的决策过程在偏头痛临床诊断中具有巨大潜力，在提高诊断准确性和辅助新诊断技术开发方面具有广阔应用前景。
