Carotid Atherosclerosis

Carotid atherosclerosis, the buildup of plaque in the carotid arteries, is a major cause of stroke, demanding accurate and efficient diagnostic methods. Current research heavily utilizes deep learning, employing convolutional and recurrent neural networks, often within multi-task frameworks that simultaneously segment plaque regions and classify plaque types (e.g., fibroatheroma, calcified plaque) from ultrasound or optical coherence tomography images. These advancements aim to improve the accuracy and speed of diagnosis, potentially leading to better risk stratification and personalized treatment strategies for patients. The development of robust, automated methods is crucial for improving patient outcomes and reducing the burden on healthcare systems.