Using AI Systems to Optimize the Clinical Outcome of Stroke Patients

This project addresses the imminent challenge of providing adequate motor rehabilitation to a growing number of stroke survivors amidst the ageing population, decreasing age of stroke, and shortage of physical/occupational therapists in Hong Kong through AI and precision rehabilitation. To reduce the socioeconomic burden from the stroke survivors' loss of independence and their care (\>HK$15 billion/year), the efficacy of rehabilitation and efficiency of its delivery must be improved. These goals can be achieved by prescribing them with individually tailored rehabilitations predicted to yield maximal functional return. Defining a predictive model for such personalization remains challenging given the immense heterogeneity of stroke. The investigators aim to build an explainable AI system that predicts a subject's recovery potential and the treatment option that may realize this potential based on multi-modal pre-rehab assessments. Data from clinical, neuroimaging, neurophysiological, and multi-omic evaluations will be collected from stroke survivors (N≥400) before they undergo upper limb rehab with usual care, neuromuscular stimulation, robotic training, or acupuncture. Machine learning-extracted data features will be used to train decision-tree and neural-network AI algorithms for robust predictions. As soon as the model is validated, the investigators will deploy it to implement a personalized rehab program in the community. Our model's ability to predict the optimal intervention from a wide spectrum of input modalities distinguishes ours from previous less-than-accurate models. Our interdisciplinary team of 13 PIs with expertise in neurology, PT/OT, acupuncture, electrical/biomed. engineering, robotics, neuroscience, neuroimaging, multi-omics, data science, and clinical trial management will put us in a world-unique position to execute this project successfully and generate opportunities of interdisciplinary education. In the long run, our prediction system will