A deep learning phenome wide association study of the electrocardiogram
Our study uncovers a range of diseases detectable in the ECG, including many previously unknown phenotypes, and makes progress towards understanding ECG features that allow this detection.
Authors
- Albert J. Rogers (ORCID: https://orcid.org/0000-0001-6585-534X)
- James Zou (ORCID: https://orcid.org/0000-0001-8880-4764)
- John Theurer
- Sulaiman Somani (ORCID: https://orcid.org/0000-0003-0913-8674)
- J. Weston Hughes (ORCID: https://orcid.org/0000-0002-7042-4031)
- Sneha S. Jain (ORCID: https://orcid.org/0000-0002-0592-1453)
- Miloš Vukadinovic (ORCID: https://orcid.org/0000-0002-0431-1493)
- Guson Kang (ORCID: https://orcid.org/0000-0002-8938-7883)
- Zaniar Ghazizadeh (ORCID: https://orcid.org/0000-0001-6747-4727)
- Bruna Gomes (ORCID: https://orcid.org/0000-0002-3349-927X)
- Jack W. O’Sullivan (ORCID: https://orcid.org/0000-0003-3629-2546)
- Michael Salerno (ORCID: https://orcid.org/0000-0001-7051-1031)
- Marco Valentin Perez (ORCID: https://orcid.org/0000-0003-2746-0202)
- Euan Angus Ashley (ORCID: https://orcid.org/0000-0001-9418-9577)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
Institutions
- Cedars-Sinai Medical Center (US)
- University of California, Los Angeles (US)
- Stanford Medicine (US)
- Cedars-Sinai Smidt Heart Institute (US)
- Rogers (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1093/ehjdh/ztaf047
- Citations
- 4
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.55
Funders
- Boston Scientific Corporation
- Apple
- National Institutes of Health
- Johnson and Johnson
- Bristol-Myers Squibb Canada
- National Heart, Lung, and Blood Institute