Deep learning on electrocardiogram waveforms to stratify risk of obstructive stable coronary artery disease
A multi-modality DL model utilizing ECG waveforms and clinical risk factors can improve prediction of oCAD in CCD compared with risk-factor based models. Prospective research is warranted to determine whether incorporating DL methods in ECG analysis improves diagnosis of oCAD and outcomes in CCD.
Authors
- Rishi K Trivedi
- Albert J. Rogers (ORCID: https://orcid.org/0000-0001-6585-534X)
- J. Weston Hughes (ORCID: https://orcid.org/0000-0002-7042-4031)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
- I-Min Chiu (ORCID: https://orcid.org/0000-0001-6876-8217)
Institutions
- Cedars-Sinai Medical Center (US)
- Palo Alto University (US)
- Stanford Medicine (US)
- Cedars-Sinai Smidt Heart Institute (US)
- Stanford University (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1093/ehjdh/ztaf020
- Citations
- 3
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 4.16
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute