Clinical Applications of Machine Learning in Stroke Care
Stroke is a leading cause of mortality and disability in the world. Machine learning (ML) technologies have been progressively used in stroke therapy in recent years. This study looked at studies that used machine learning in stroke care to give a broad overview of the subject. Automatically examining carotid plaques (for stroke risk stratification), detecting stroke lesions on imaging, identifying possible treatment complications in patients, facilitating brain-computer-interface (BCI)-assisted rehabilitation, and predicting stroke prognosis have all been made possible thanks to advances in machine learning techniques. In domains like evaluating carotid intima-media thickness and diagnosing early damage of ischemic stroke using CT imaging, certain machine learning applications outperform doctors. Furthermore, ML applications in clinical outcome prediction perform similarly to or better than the traditional method of logistic regression. However, issues such as automated lesion segmentation, BCI-assisted rehabilitation, and long-term stroke prognosis prediction remain obstacles. Deep learning and other recently developed machine learning technologies may be helpful in overcoming the issues. To make these ML methods more accurate, generalizable, and dependable, more research is needed to test and optimise these ML applications, as well as large-sample studies and proper validation. As the need for precision medicine in stroke grows and machine learning technology advances, it is expected that machine learning applications will be released to improve computer-aided stroke prevention and transform traditional stroke medicine into data-driven personalised stroke management, which will lower morbidity and mortality rates, improve stroke care, and save lives.
Author (S) Details
Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA.
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