Interpretable Artificial Intelligence Analysis of Functional Magnetic Resonance Imaging for Migraine Classification: Quantitative Study.

ABSTRACT:

Background

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.

Objective

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

SUBMITTER: Li G 

PROVIDER: S-EPMC12444220 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature