Automated multilabel diagnosis on electrocardiographic images and signals

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 and Rohan Khera ()
Additional contact information
Veer Sangha: Yale University
Bobak J. Mortazavi: Texas A&M University
Adrian D. Haimovich: Yale University School of Medicine
Antônio H. Ribeiro: Uppsala University
Cynthia A. Brandt: Yale University School of Medicine
Daniel L. Jacoby: Yale School of Medicine
Wade L. Schulz: Center for Outcomes Research and Evaluation, Yale-New Haven Hospital
Harlan M. Krumholz: Center for Outcomes Research and Evaluation, Yale-New Haven Hospital
Antonio Luiz P. Ribeiro: Telehealth Center and Cardiology Service, Hospital das Clínicas
Rohan Khera: Center for Outcomes Research and Evaluation, Yale-New Haven Hospital

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29153-3

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