ArticleInformation sciences2025
A multimodal machine learning approach to predict Fugl-Meyer scores and motor recovery potential in stroke rehabilitation: Toward precision-based therapies.
Article in Information sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Stroke is a leading cause of long-term disability, with highly variable recovery trajectories and challenges in prediction and monitoring. Frequently used measures (e.g., National Institute of Health Stroke Scale (NIHSS) and Fugl-Meyer (FM) assessment of motor impairment) have significant limitations. As the societal burden of stroke increases, developing robust methodologies for assessing and predicting recovery is essential to optimize treatment plans and improve outcomes. This paper presents our Integrated Motion Analysis Suite (IMAS), which leverages multimodal data (clinical, sensor, and neuroimaging inputs) and multimodal machine learning (MML) to predict FM scores and motor recovery in stroke. Its potential is demonstrated via analysis of 28 stroke patients in acute and subacute phases of recovery, where features extracted from a set of motor tasks were used to predict FM scores and motor recovery, achieving a coefficient of determination (R
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