Stroke Prediction

Stroke prediction research aims to improve the accuracy and timeliness of identifying individuals at high risk of stroke, enabling preventative measures and optimizing treatment strategies. Current efforts focus on developing sophisticated machine learning models, including random forests, XGBoost, xDeepFM, and transformer networks, leveraging multimodal imaging data (CT, MRI, CTA) and clinical records to predict stroke occurrence, lesion extent, treatment response (e.g., recanalization after thrombectomy), and long-term outcomes (mortality and morbidity). These advancements hold significant promise for improving clinical decision-making, personalizing stroke care, and ultimately reducing the burden of this devastating condition.