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

Abstract

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.

Methods: We retrospectively analyzed 242 patients with acute ischemic stroke who underwent brain magnetic resonance imaging and motor function assessment. Clinical and imaging variables-including infarct volume, motor tract involvement, and subtype classification-were extracted using automated software. Predictive models for Fugl-Meyer Assessment upper extremity (FMA-UE) scores were developed using hierarchical regression, and K-means clustering was applied to standardized baseline data. Cluster characteristics were compared, and recovery profiles were visualized.

Results: Incorporating imaging variables improved prediction of FMA-UE scores (adjusted R²: 0.595), with motor tract lesion volume emerging as a key predictor. Cluster analysis revealed three distinct patient subgroups: Cluster 1 (mild impairments, younger age), Cluster 2 (older patients with multiple comorbidities), and Cluster 3 (severe neurological deficits, large infarcts). Functional outcomes differed significantly across clusters, with Cluster 3 showing the greatest disability (76% with follow-up modified Rankin Scale 4-6, P < 0.001).

Conclusion: Imaging-derived features from early magnetic resonance imaging improve the prediction of upper limb motor outcomes after stroke. Cluster-based stratification identifies clinically meaningful subgroups, supporting precision rehabilitation through personalized care planning and targeted secondary prevention.