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Automated multilabel diagnosis on electrocardiographic images and signals

Author

Listed:
  • Veer Sangha

    (Yale University)

  • Bobak J. Mortazavi

    (Texas A&M University
    Center for Outcomes Research and Evaluation, Yale-New Haven Hospital)

  • Adrian D. Haimovich

    (Yale University School of Medicine)

  • Antônio H. Ribeiro

    (Uppsala University)

  • Cynthia A. Brandt

    (Yale University School of Medicine
    VA Connecticut Healthcare System)

  • Daniel L. Jacoby

    (Yale School of Medicine)

  • Wade L. Schulz

    (Center for Outcomes Research and Evaluation, Yale-New Haven Hospital
    Yale School of Medicine)

  • Harlan M. Krumholz

    (Center for Outcomes Research and Evaluation, Yale-New Haven Hospital
    Yale School of Medicine
    Yale School of Public Health)

  • Antonio Luiz P. Ribeiro

    (Telehealth Center and Cardiology Service, Hospital das Clínicas
    Faculdade de Medicina, Universidade Federal de Minas Gerais)

  • Rohan Khera

    (Center for Outcomes Research and Evaluation, Yale-New Haven Hospital
    Yale School of Medicine)

Abstract

The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use. A total of 2,228,236 12-lead ECGs signals from 811 municipalities in Brazil are transformed to ECG images in varying lead conformations to train a convolutional neural network (CNN) identifying 6 physician-defined clinical labels spanning rhythm and conduction disorders, and a hidden label for gender. The image-based model performs well on a distinct test set validated by at least two cardiologists (average AUROC 0.99, AUPRC 0.86), an external validation set of 21,785 ECGs from Germany (average AUROC 0.97, AUPRC 0.73), and printed ECGs, with performance superior to signal-based models, and learning clinically relevant cues based on Grad-CAM. The model allows the application of AI to ECGs across broad settings.

Suggested Citation

  • Veer Sangha & Bobak J. Mortazavi & Adrian D. Haimovich & Antônio H. Ribeiro & Cynthia A. Brandt & Daniel L. Jacoby & Wade L. Schulz & Harlan M. Krumholz & Antonio Luiz P. Ribeiro & Rohan Khera, 2022. "Automated multilabel diagnosis on electrocardiographic images and signals," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29153-3
    DOI: 10.1038/s41467-022-29153-3
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    References listed on IDEAS

    as
    1. Antônio H. Ribeiro & Manoel Horta Ribeiro & Gabriela M. M. Paixão & Derick M. Oliveira & Paulo R. Gomes & Jéssica A. Canazart & Milton P. S. Ferreira & Carl R. Andersson & Peter W. Macfarlane & Wagner, 2020. "Automatic diagnosis of the 12-lead ECG using a deep neural network," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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