ArticleEuropean heart journal. Digital health2022
Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- [Atrial fibrillation screening-reading the P-wave].Innere Medizin (Heidelberg, Germany) · 2026Review
- Review
- Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients.Kidney360 · 2026Article
- Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.European heart journal. Digital health · 2026Article
- Artificial Intelligence-Based Electrocardiogram Model as a Predictor of Postoperative Atrial Fibrillation Following Cardiac Surgery: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Expert Review of the Strategies to Optimize Long-Term Outcomes After Coronary Artery Bypass Grafting.Reviews in cardiovascular medicine · 2025Review
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Sequential AI-ECG Diagnostic Protocol for Opportunistic Atrial Fibrillation Screening: A Retrospective Single-Center Study.Journal of clinical medicine · 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
- 2024 transplant AI symposium: key AI models shaping the future of transplant care.Frontiers in transplantation · 2025Article
- Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.Heart rhythm O2 · 2024Article
- Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm.Reviews in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results: We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA Conclusion: The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.
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