Trial reportIEEE journal of translational engineering in health and medicine2025
Translational Evaluation of a Machine Learning-Based Interactive Lab for Aphasia Rehabilitation in Post Stroke Patients.
Trial report in IEEE journal of translational engineering in health and medicine, 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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5 authors.
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Abstract
objectiveTo address the limitations of conventional aphasia therapy by developing and clinically evaluating a machine learning based interactive lab for personalized rehabilitation in post-stroke patients. METHODS AND PROCEDURES: A four week clinical trial was conducted with 27 aphasia patients, randomly assigned to an experimental group ([Formula: see text]) using the Language Interactive Lab and a control group ([Formula: see text]) receiving conventional therapy. Language performance was assessed using the Chinese Communicative Aphasia Test (CCAT). System interaction data were also used to train classifiers for aphasia severity and recovery tracking.
resultsThe experimental group showed statistically significant improvements in 7 out of 9 CCAT subtests ([Formula: see text]) and a highly significant total score increase ([Formula: see text]) compared to the control group. Machine learning classifiers achieved up to 91.7% accuracy in predicting aphasia severity and recovery progression.
conclusionThe proposed interactive lab integrates gamified therapy with real time, explainable machine learning assessment, demonstrates clinical efficacy in improving language outcomes, and offers a scalable framework for AI-driven, adaptive neurorehabilitation that has been clinically validated within a hospital setting and designed to align with Taiwan Food and Drug Administration (TFDA) software-as-a-medical-device (SaMD) regulatory principles for translational deployment in clinical environments and hospital investigational use guidelines. Clinical Impact-The integration of gamified digital therapy with machine learning analytics supports personalized, data driven intervention for aphasia rehabilitation in both clinical and home settings, particularly in resource limited environments. Clinical and Translational Impact Statement-This study supports Clinical Research by demonstrating that AI-powered digital therapy significantly improves language outcomes in post-stroke aphasia patients and offers a pathway to scalable, at home neurorehabilitation.
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