The deep learning model EchoNet accurately identified pacemaker leads (AUC=0.89), enlarged left atrium (AUC=0.85), and predicted systemic phenotypes like sex (AUC=0.88) from echocardiograms.
Can a deep learning model (EchoNet) accurately interpret echocardiograms to identify cardiac structures, estimate function, and predict systemic phenotypes?
The EchoNet deep learning model can accurately interpret echocardiograms to assess cardiac structure and function, and predict systemic phenotypes, offering a tool to standardize and streamline clinical workflows.
Abstract Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.85), normal left ventricular wall thickness (AUC = 0.75), left ventricular end systolic and diastolic volumes( R 2 = 0.73 and R 2 = 0.68), and ejection fraction ( R 2 = 0.48) as well as predicted systemic phenotypes of age ( R 2 = 0.46), sex (AUC = 0.88), weight ( R 2 = 0.56), and height ( R 2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlight hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, standardize interpretation in areas with insufficient qualified cardiologists, and more consistently produce echocardiographic measurements.
Ghorbani et al. (Mon,) reported a other. EchoNet (Deep learning model) was evaluated on Identification of cardiac structures, estimation of cardiac function, and prediction of systemic phenotypes. The deep learning model EchoNet accurately identified pacemaker leads (AUC=0.89), enlarged left atrium (AUC=0.85), and predicted systemic phenotypes like sex (AUC=0.88) from echocardiograms.