Stroke Data

Stroke data analysis focuses on leveraging neuroimaging (CT, MRI) and clinical data to improve stroke diagnosis, prognosis, and treatment. Current research emphasizes developing and validating machine learning models, including graph convolutional networks and random forests, for tasks like lesion segmentation, brain connectivity analysis, and risk prediction, often utilizing synthetic data to address data scarcity issues. These advancements aim to enhance the accuracy and efficiency of stroke care, potentially leading to improved patient outcomes and reducing health disparities through better risk stratification and personalized treatment strategies.