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

Learning Objectives:

• Describe how automated brain MRI segmentation and quantitative imaging biomarkers can enhance prediction of post-stroke upper limb motor impairment.
• Explain how unsupervised machine learning, particularly K-means clustering, can identify clinically meaningful recovery subgroups in stroke patients.
• Discuss how imaging-derived features can contribute to precision rehabilitation strategies based on individualized patient profiles.