Echocardiogram Interpretation

Echocardiogram interpretation, the analysis of ultrasound images of the heart, aims to automate the diagnosis and quantification of cardiac function, reducing human error and improving efficiency. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and variational autoencoders (VAEs) to analyze both 2D and video echocardiograms, often incorporating multimodal data (e.g., Doppler imaging) and self-supervised learning techniques. These advancements enable automated measurements of key parameters like ejection fraction and facilitate the differentiation of cardiac conditions such as hypertrophic cardiomyopathy and cardiac amyloidosis, ultimately improving diagnostic accuracy and patient care.