Stroke Detection

Stroke detection research focuses on developing faster, more accurate methods for diagnosing and predicting stroke severity, improving patient outcomes. Current efforts utilize diverse approaches, including machine learning models (e.g., deep convolutional neural networks, random forests, and recurrent neural networks) applied to various data modalities such as CT scans, MRI images, and even egocentric video recordings from smart glasses. These advancements aim to enhance diagnostic capabilities, personalize treatment strategies, and ultimately reduce the significant morbidity and mortality associated with stroke.