A comprehensive review of multimodal techniques for early detection of minor strokes

Minor strokes, usually in the form of transient neurological interruptions, are key early warning signs of impending larger ischemic attacks. Without intervention, these events can progress to full-scale strokes, with major longterm ramifications. Early identification of minor strokes is necessary to prevent further advancement and improve patient results. In this review, multimodal methods of identifying the main predictors of minor strokes: facial asymmetry, speech disturbance, and arm weakness are discussed. It compares machine learning and conventional approaches to identifying these symptoms, individually and in conjunction. Progress in facial impairment detection has increasingly depended on deep learning methods, whereas speech analysis models determine dysarthria by examining rhythm, pitch, and articulation. In detecting arm weakness, wearable sensor technologies have been promising in recording real-time limb movement data. Mobile health apps combine these tools to enhance accessibility and constant surveillance. There are challenges that include dataset constraints, model generalizability, and clinical validation. This review gives an extensive overview of these multimodal strategies, highlighting their promise and challenges that need to be addressed for successful real-world use for detecting minor strokes.