Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke
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Abstract
BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.
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AI-ECG prediction of future AF could enrich for patients at risk of AF-related stroke.
“Raghunath et al. showed that AI-ECG prediction of future AF could also enrich for patients at risk of AF-related stroke, supporting the potential use of these models for targeted screening and rhythm monitoring [13].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [13]
Contemporary examples of clinical applications in cardiology include the use of ECGs to predict left ventricular dysfunction or future development of atrial fibrillation (AF).
“Contemporary examples of these clinical applications in cardiology include the use of ECGs to predict left ventricular dysfunction or future development of atrial fibrillation (AF).[13][14][15][16]”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [13]
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- Authors
- Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., Rocha, D. B., Stoudt, N. J., Schneider, G., Johnson, K. W., Zimmerman, N., Leader, J. B., Kirchner, H. L., Griessenauer, C. J., Hafez, A., Good, C. W., Fornwalt, B. K., & Haggerty, C. M.
- Affiliations
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA.
- Heart and Vascular Center, Evangelical Hospital, Lewisburg, PA (J.M.P.).
- Tempus Labs (United States) (US)
- Phenomic Analytics and Clinical Data Core (D.N.H., B.F.L., D.B.R., J.B.L.), Geisinger, Danville, PA.
- Geisinger Health System (US)
- Paracelsus Medical University (AT)
- UPMC Hamot (US)
- Corresponding author
- Haggerty, Christopher M.
- ORCID
- Venue
- Circulation
- Published
- 2021-03-30
- Volume
- 143
- Issue
- 13
- Pages
- 1287-1298
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC7996054
- OpenAlex
- W3133470659
- Cited by
- 312
- References
- 50
- Citations by year
- 2026: 56 · 2025: 74 · 2024: 65 · 2023: 51 · 2022: 40 · 2021: 25 · 2020: 1
- FWCI
- 30.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Circulation (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- new-onset atrial fibrillation, atrial fibrillation prediction, deep neural network, 12-lead ECG, stroke risk stratification, atrial fibrillation screening, digital ECG
- MeSH terms
- Deep Learning, Atrial Fibrillation, Electrocardiography, Female, Humans, Male, Survival Analysis, Neural Networks, Computer, Stroke
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