ArticleScientific reports2021
A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- Prediction of Atrial Fibrillation Occurrence With Handheld Mobile Electrocardiogram: Deep Learning Model Development Using Real-World Data.JMIR medical informatics · 2026Article
- Artificial Intelligence for Prediction and Detection of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms and Ambulatory Monitoring.Biomedicines · 2026Review
- Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.European heart journal. Digital health · 2026Article
- CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence-Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2026Article
- Artificial Intelligence Applied to Electrocardiograms Recorded in Sinus Rhythm for Detection and Prediction of Atrial Fibrillation: A Scoping Review.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Short-term atrial fibrillation onset prediction using machine learning.European heart journal. Digital health · 2025Article
- Review
- Clinical Perspectives on Stroke Prevention in Patients with Non-valvular Atrial Fibrillation in the Philippines.Cureus · 2025Review
- Electrocardiogram abnormalities and CVD mortality: prospective cohort study of Tehran lipid and glucose study.BMC public health · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.Open heart · 2025Article
- Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.The Korean journal of internal medicine · 2025Article
- Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
- Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.Frontiers in cardiovascular medicine · 2025Article
- Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction.Frontiers in cardiovascular medicine · 2025Article
- Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.BMC medical research methodology · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.
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