Deep Learning for Automated Echocardiogram Analysis

Abstract

In the fight of heart disease (the #1 global killer), Left ventricular ejection fraction (EF) calculated via echocardiography plays crucial role for disease diagnosis and treatment because EF can distinguish heart failure from normal cardiac function. However, traditional EF calculation is a time-consuming manual process with high variability ‒ a single routine echocardiogram generates about 10–50 videos (~3,000 images), coupled with the limited capacity to analyze such large dataset by human experts often results in misdiagnosis. This project aimed to develop transparent and interpretable deep learning pipeline for automated echocardiogram analysis to calculate EF for quick assessment of cardiac function. Using the EchoNet dataset, five PyTorch deep learning models were trained to automatically calculate EF achieving mean error rates that ranged from 14-16%, comparable to that of expert cardiac sonographers, and significantly outperformed qualitative analysis by physicians (~30% error rate). Among the five models, MobileNet was identified as the best deep learning model for web application and portable devices; therefore, it was deployed as a web-based app through AWS, standalone PC, and Raspberry Pi, enabling upload and analyze echocardiogram videos and obtain EF calculation results within seconds. Such automated echocardiogram analysis can dramatically speed up image analysis, reduce the burden on cardiologists, eliminate inter-observer variability, hence democratize echocardiography by enabling non-experts to quickly and accurately assess cardiac functions at point of care even in cardiology expertise limited rural areas and developing countries. Future work includes improving these models with additional data and adapting the app for handheld ultrasound devices.