Imaging-Enhanced Prediction of Upper Limb Motor Function After Stroke Using Automated Segmentation and Cluster Analysis

Background

Stroke is a leading cause of long-term disability, and early prediction of motor outcomes is critical for guiding individualized rehabilitation strategies. Although neuroimaging offers prognostic insights, its clinical integration remains limited. This study aimed to evaluate whether explainable imaging-derived features can improve the prediction of upper limb motor impairment and to identify distinct recovery profiles through cluster analysis.