Document Type
Article
Publication Date
1-29-2026
Abstract
BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG).
OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography.
METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic diameter stenosis ≥70% in the left anterior descending, left circumflex, or right coronary artery or ≥50% in the left main coronary artery. We then applied the model to an external validation set.
RESULTS: In the cross-validation cohort, the prevalence of clinically significant CAD was 64.5%; the model achieved a positive predictive value of 91.7% (95% CI: 89.9%-93.4%), negative predictive value of 72.8% (95% CI: 69.6%-76.0%), and area under the curve of 91.4% (95% CI: 89.4%-94.4%) in predicting clinically significant CAD. In external validation, the prevalence of clinically significant CAD was 36.0%; the model achieved a positive predictive value of 82.5% (95% CI: 75.9%-89.2%), negative predictive value of 88.1% (95% CI: 84.0%-92.1%), and area under the curve of 92.4% (95% CI: 89.7%-95.1%) in predicting clinically significant CAD.
CONCLUSIONS: This study demonstrated the clinical utility of a deep learning artificial intelligence algorithm to analyze a digital 12-lead ECG to predict the presence of clinically significant CAD as determined by coronary angiography.
Recommended Citation
Leasure, Michael; Poornima, Indu; Butchy, Adam; Jain, Utkars; Vasoya, Devin; Warnick, Michael; Williams, Brent; Rehder, John; Menon, Prahlad; Covalesky, Veronica A.; and Mintz, Gary S., "Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model" (2026). Department of Medicine Faculty Papers. Paper 542.
https://jdc.jefferson.edu/medfp/542
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
PubMed ID
41616590
Language
English
Included in
Artificial Intelligence and Robotics Commons, Cardiology Commons, Cardiovascular Diseases Commons, Investigative Techniques Commons
Comments
This article is the author’s final published version in JACC: Advances, Volume 5, Issue 3, 2026, Article number 102573.
The published version is available at https://doi.org/10.1016/j.jacadv.2025.102573. Copyright © 2026 The Authors.