Machine learning in stroke and its sequelae: a narrative review of clinical applications and emerging trends.

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Abstract

This narrative review synthesizes machine learning (ML) applications across the stroke and post-stroke continuum from acute imaging and diagnosis to long-term sequelae prognosis and rehabilitation. We searched PubMed, Embase, and WOS from inception to October 17, 2025, for a comprehensive review. We used a combination of search terms, including "machine learning," "deep learning," "post stroke." These terms were carefully selected to capture a wide range of relevant studies and articles related to stroke and ML. ML has been successfully deployed in six core domains: Image reading, where deep learning enables automated lesion segmentation on MRI/CT and prediction of tissue fate; Diagnosis, including etiology, atrial fibrillation screening; Overall prognosis, with high-accuracy models for functional outcome, mortality, and readmission; Sequelae prediction, such as cognitive impairment, motor dysfunction, aphasia, depression, fatigue, and organ diseases; Treatment response, including outcome prediction after thrombectomy and rehabilitation; Rehabilitation monitoring, using wearable sensors and robotics for objective, granular assessment of motor recovery. A clear trend toward multimodal data integration and model interpretability was observed, enhancing both predictive power and biological plausibility. ML has evolved from a research tool into a transformative force in stroke care, enabling precise, individualized prediction and monitoring across the entire post-stroke trajectory. Future efforts must prioritize prospective validation, standardized reporting, and seamless integration into clinical workflows to realize its full potential for precision medicine.

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