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利用多模式基础模型图像编码器来增强基于脑MRI的头痛分类

Fazle Rafsani^1,2, Devam Sheth^1,2, Yiming Che^1,2

¹School of Computing and Augmented Intelligence, Arizona State University, 699 S. Mills Ave, Tempe, AZ, 85287, USA.

Scientific reports

|September 26, 2025

概括

这项研究介绍了一种使用脑MRI扫描来检测偏头痛等头痛的AI模型. 人工智能准确地识别了头痛类型,为与这些疾病相关的大脑变化提供了新的见解.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

头痛很常见,通常通过症状来诊断.
先进的成像和人工智能为诊断提供了新的可能性.
现有的人工智能模型往往需要大量的数据集,这限制了它们的使用.

研究的目的:

通过使用大脑MRI数据微调预训练的AI模型 (BioMedCLIP) 来进行头痛分类.
从结构性MRI中检测潜在的头痛生物标志物.
为了克服AI模型对头痛疾病的训练数据的局限性.

主要方法:

借助BioMedCLIP,一个多式联络基础模型与视觉变压器 (ViT) 和PubMedBERT.
微调了ViT模型在721个人的数据集上 (297名当地参与者 + 424名IXI健康对照).
将偏头痛,急性创伤后头痛 (APTH) 和持续性创伤后头痛 (PPTH) 与使用五倍交叉验证的健康对照进行分类.

主要成果:

实现了高精度:89.96%的偏头痛与HC相比,88.13%的APTH与HC相比,83.13%的PPTH与HC相比.
确定了用于使用梯度加权类激活映射 (Grad-CAM) 进行分类的关键大脑区域.
偏头痛的分类涉及中后皮层,上边环,上皮层和前皮层.

结论:

这项研究是第一个使用多模式生物医学基础模型进行头痛分类和从结构性MRI中检测生物标志物的研究.
人工智能模型在将头痛类型与健康对照区分方面表现出高准确度.
这些发现为头痛疾病的神经解剖学相关性提供了新的见解.

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