Heart Sound Auscultation

Heart sound auscultation, the process of listening to heart and lung sounds for diagnostic purposes, is undergoing a transformation driven by advancements in machine learning. Current research focuses on developing sophisticated algorithms, including deep learning models like transformers and convolutional neural networks, to automatically analyze these sounds, often incorporating multimodal data such as echocardiography reports or patient medical records to improve diagnostic accuracy. This automated analysis aims to improve the efficiency and accessibility of auscultation, potentially assisting clinicians in detecting cardiovascular and respiratory diseases earlier and more reliably, particularly in resource-constrained settings.