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

Author

Listed:
  • David Ouyang

    (Stanford University)

  • Bryan He

    (Stanford University)

  • Amirata Ghorbani

    (Stanford University)

  • Neal Yuan

    (Cedars-Sinai Medical Center)

  • Joseph Ebinger

    (Cedars-Sinai Medical Center)

  • Curtis P. Langlotz

    (Stanford University
    Stanford University)

  • Paul A. Heidenreich

    (Stanford University)

  • Robert A. Harrington

    (Stanford University)

  • David H. Liang

    (Stanford University
    Stanford University)

  • Euan A. Ashley

    (Stanford University
    Stanford University)

  • James Y. Zou

    (Stanford University
    Stanford University
    Stanford University)

Abstract

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease1, screening for cardiotoxicity2 and decisions regarding the clinical management of patients with a critical illness3. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training4,5. 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.

Suggested Citation

  • David Ouyang & Bryan He & Amirata Ghorbani & Neal Yuan & Joseph Ebinger & Curtis P. Langlotz & Paul A. Heidenreich & Robert A. Harrington & David H. Liang & Euan A. Ashley & James Y. Zou, 2020. "Video-based AI for beat-to-beat assessment of cardiac function," Nature, Nature, vol. 580(7802), pages 252-256, April.
  • Handle: RePEc:nat:nature:v:580:y:2020:i:7802:d:10.1038_s41586-020-2145-8
    DOI: 10.1038/s41586-020-2145-8
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    Cited by:

    1. Md Tauhidul Islam & Lei Xing, 2023. "Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Jun Ma & Yuting He & Feifei Li & Lin Han & Chenyu You & Bo Wang, 2024. "Segment anything in medical images," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Md Tauhidul Islam & Zixia Zhou & Hongyi Ren & Masoud Badiei Khuzani & Daniel Kapp & James Zou & Lu Tian & Joseph C. Liao & Lei Xing, 2023. "Revealing hidden patterns in deep neural network feature space continuum via manifold learning," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    4. Jasper Tromp & David Bauer & Brian L. Claggett & Matthew Frost & Mathias Bøtcher Iversen & Narayana Prasad & Mark C. Petrie & Martin G. Larson & Justin A. Ezekowitz & Scott D. Solomon, 2022. "A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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