Video-based AI for beat-to-beat assessment of cardiac function.

Journal: Nature
Published Date: Mar 25, 2020

Abstract

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training. Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

Authors

  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Bryan He
    Department of Computer Science, Stanford University, Stanford, California.
  • Amirata Ghorbani
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Neal Yuan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Joseph Ebinger
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.
  • Paul A Heidenreich
    Veterans Administration Palo Alto Health Care System, Palo Alto, California.
  • Robert A Harrington
    Department of Medicine, Stanford University, Stanford, CA.
  • David H Liang
    Department of Medicine, Stanford University, Stanford, CA, USA.
  • Euan A Ashley
    Department of Genetics, Stanford University, Stanford, California, United States of America.
  • James Y Zou
    Department of Computer Science, Stanford University, Stanford, CA, USA. jamesz@stanford.edu.

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