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Automated multilabel diagnosis on electrocardiographic images and signals

Sangha, Veer (author)
Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA.
Mortazavi, Bobak J. (author)
Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX USA.;Yale New Haven Med Ctr, Ctr Outcomes Res & Evaluat, 20 York St, New Haven, CT 06504 USA.
Haimovich, Adrian D. (author)
Yale Univ, Sch Med, Dept Emergency Med, New Haven, CT USA.
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Horta Ribeiro, Antônio (author)
Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens
Brandt, Cynthia A. (author)
Yale Univ, Sch Med, Dept Emergency Med, New Haven, CT USA.;VA Connecticut Healthcare Syst, West Haven, CT USA.
Jacoby, Daniel L. (author)
Yale Sch Med, Dept Internal Med, Sect Cardiovasc Med, New Haven, CT 06510 USA.
Schulz, Wade L. (author)
Yale New Haven Med Ctr, Ctr Outcomes Res & Evaluat, 20 York St, New Haven, CT 06504 USA.;Yale Sch Med, Dept Lab Med, New Haven, CT USA.
Krumholz, Harlan M. (author)
Yale New Haven Med Ctr, Ctr Outcomes Res & Evaluat, 20 York St, New Haven, CT 06504 USA.;Yale Sch Med, Dept Internal Med, Sect Cardiovasc Med, New Haven, CT 06510 USA.;Yale Sch Publ Hlth, Dept Hlth Policy & Management, New Haven, CT USA.
Ribeiro, Antonio Luiz P. (author)
Hosp Clin Sao Paulo, Telehlth Ctr & Cardiol Serv, Sao Paulo, Brazil.;Univ Fed Minas Gerais, Fac Med, Dept Internal Med, Belo Horizonte, MG, Brazil.
Khera, Rohan (author)
Yale New Haven Med Ctr, Ctr Outcomes Res & Evaluat, 20 York St, New Haven, CT 06504 USA.;Yale Sch Med, Dept Internal Med, Sect Cardiovasc Med, New Haven, CT 06510 USA.
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Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX USA.;Yale New Haven Med Ctr, Ctr Outcomes Res & Evaluat, 20 York St, New Haven, CT 06504 USA. (creator_code:org_t)
2022-03-24
2022
English.
In: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • The application of artificial intelligence for automated diagnosis of electrocardiograms can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. Here, the authors report the development of a multi-label automated diagnosis model for electrocardiographic images. 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Engineering (hsv//eng)

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