ArticleNPJ digital medicine2024
Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Derived ECG-Age as a Predictor of Mortality and Cardiovascular Events: A Systematic Review and Meta-Analysis.Arquivos brasileiros de cardiologia · 2026Pooled it
- Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data.Journal of arrhythmia · 2026Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.European heart journal. Digital health · 2026Article
- Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.European heart journal. Digital health · 2026Review
- Wearable device derived electrocardiographic age and its association with atrial fibrillation.NPJ digital medicine · 2026Article
- Transthoracic echocardiographic and artificial intelligence-enabled electrocardiography predictors of atrial arrhythmia recurrence after surgical ablation.Heart rhythm O2 · 2026Article
- Artificial intelligence-enabled electrocardiographic 'sex discrepancy' as a predictor of atrial fibrillation recurrence: contextualising the findings of parkEuropean heart journal. Digital health · 2026Article
- Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health.Communications medicine · 2025Article
- Dynamic changes of artificial intelligence estimated electrocardiographic age and sex predicts recurrence after atrial fibrillation catheter ablation.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
- Targeting the Substrate: Mechanism-Based Ablation Strategies for Persistent Atrial Fibrillation.Journal of clinical medicine · 2025Review
- Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.