Work

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

Year: 2026

Type: article

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 R 2 : 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.

Source: Journal of Korean Medical Science

DOI: 10.3346/jkms.2026.41.e263

Authors: Seungwoo Cha, Wi-Sun Ryu, Bokgi Hwang, Dae Yul Kim, Seung Hak Lee

Institutions: Asan Medical Center, University of Ulsan

Language: English

Topic: Stroke Rehabilitation and Recovery

Subfield: Rehabilitation

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $690