Transesophageal Echocardiography
Transesophageal echocardiography (TEE) is a crucial cardiac imaging technique used for diagnosis and intervention, but its reliance on expert interpretation and inherent limitations in image quality hinder widespread application. Current research focuses on improving TEE analysis through deep learning models, including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs), to automate tasks like view classification, valve detection, and ejection fraction calculation, as well as to enhance image quality and tracking. These advancements aim to improve diagnostic accuracy, reduce variability between clinicians, and ultimately enhance patient care, particularly in resource-limited settings where experienced sonographers are scarce.