Stroke Assessment

Stroke assessment aims to rapidly and accurately diagnose stroke, enabling timely treatment and improving patient outcomes. Current research focuses on developing automated assessment tools using various machine learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and ensemble methods, often incorporating multimodal data such as MRI scans, video recordings, and clinical data. These advancements leverage image segmentation, action recognition, and clinical data fusion to improve diagnostic accuracy and efficiency, potentially leading to more effective stroke management and rehabilitation strategies.