Cardiac Function

Cardiac function assessment aims to accurately quantify the heart's performance, primarily focusing on parameters like ejection fraction and ventricular volumes, using imaging modalities such as echocardiography and cardiac MRI. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, transformers, and variational autoencoders, often within semi-supervised or unsupervised learning frameworks to address data scarcity and improve robustness. These advancements enable automated, efficient analysis of cardiac images, potentially improving diagnostic accuracy, streamlining clinical workflows, and facilitating more precise patient stratification for improved cardiovascular care.