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Updated: Aug 6, 2026
Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
Published on: February 22, 2020
Domain-specific functional outcome prediction in stroke rehabilitation: A multicenter artificial intelligence study
Dong-Young Kim1, Seyoung Shin2, Junhyung Moon3
1Medical AI Research Center, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, South Korea.
Background:
Stroke is a leading cause of long-term disability worldwide, yet existing clinical decision support tools rely on global disability metrics, such as the modified Rankin Scale, which do not adequately reflect patient-centered rehabilitation or recovery goals.
Objective:
We aimed to develop, validate, and implement artificial intelligence (AI)-based clinical support models for predicting domain-specific functional outcomes after stroke across multiple rehabilitation institutions and timepoints.
Methods:
We utilized prospective and retrospective multicenter data collected from patients with stroke across four rehabilitation institutions in South Korea (2017-2024). Prognostic models were developed for three functional domains-ambulation, cognitive function, and activities of daily living-under two temporal scenarios: acute-to-subacute and acute-to-early-chronic prediction. Alignment-based regularization was applied to improve cross-institutional generalizability.
Results:
The proposed framework achieved strong predictive performance in internal validation (AUROC up to 0.897 for ambulation and 0.864 for cognition) and favorable external validation performance, with AUROCs up to 0.924 (ADLs) and 0.892 (cognition), despite institution-specific differences in cohort size and variable availability across centers. The implemented web-based clinical decision support system provides real-time prediction of individualized recovery trajectory with intuitive visualization designed to support clinician-patient communication.
Conclusions:
Our findings demonstrate the predictive feasibility of AI-based modeling for domain-specific stroke prognosis and present a prototype implementation illustrating the potential clinical applicability of the proposed framework across heterogeneous institutional settings. The use of routinely collected clinical variables and the preliminary cross-institutional validation results support further investigation of real-world rehabilitation practices, pending prospective clinical evaluation.