Deep learning interpretation of echocardiograms

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 = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end…

• NS
  National Science Foundation

  Awards: 1763191, CCF 1763191
• AC
  American College of Cardiology Foundation
• NI
  National Institutes of Health

  Awards: R21 MD012867-01, P30AG059307