An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Abstract
Background Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. Methods We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, spe…
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Increasing AI specificity reduces unnecessary referrals and associated costs but may compromise detection capability.
“Improving AI’s sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [11]
Increasing AI sensitivity increases detection of high-risk patients but may raise medical costs.
“Improving AI’s sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [11]
The ECG may contain latent signatures of atrial remodeling or electrophysiologic vulnerability even when AF is not present on the tracing.
“However, Attia et al. demonstrated that an AI-enabled algorithm could identify AF-associated patterns from standard 12-lead ECGs recorded during sinus rhythm, suggesting that the ECG may contain latent signatures of atrial remodeling or electrophysiologic vulnerability even when AF is not present on the tracing itself [12].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [12]
An AI-enabled algorithm could identify AF-associated patterns from standard 12-lead ECGs recorded during sinus rhythm.
“However, Attia et al. demonstrated that an AI-enabled algorithm could identify AF-associated patterns from standard 12-lead ECGs recorded during sinus rhythm, suggesting that the ECG may contain latent signatures of atrial remodeling or electrophysiologic vulnerability even when AF is not present on the tracing itself [12].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [12]
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- Authors
- Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., Kapa, S., & Friedman, P. A.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Friedman, Paul A
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 394
- Issue
- 10201
- Pages
- 861-867
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W2965520043
- Cited by
- 1,613
- References
- 34
- Citations by year
- 2026: 267 · 2025: 311 · 2024: 264 · 2023: 224 · 2022: 190 · 2021: 198 · 2020: 141 · 2019: 15
- FWCI
- 88.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Mayo Clinic
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- atrial fibrillation, sinus rhythm, convolutional neural networks, artificial intelligence, atrial flutter, point-of-care screening, asymptomatic atrial fibrillation, stroke risk, heart failure risk, mortality risk, outcome prediction, risk stratification, digital ECG, 12-lead ECG, machine learning classification
- MeSH terms
- Adult, Aged, Algorithms, Atrial Fibrillation, Atrial Flutter, Electrocardiography, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, ROC Curve, Case-Control Studies, Neural Networks, Computer
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