ArticleCirculation. Arrhythmia and electrophysiology2020
Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.
Article in Circulation. Arrhythmia and electrophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2023Pooled it
- Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.BMC cardiovascular disorders · 2023Trial
- The Hodgkin-Huxley-Katz Prize Lecture: Heart digital twins: A new paradigm in arrhythmia medicine.The Journal of physiology · 2026Article
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Can atrial fibrillation ablation outcomes be properly predicted with electrocardiography and artificial intelligence?European heart journal. Digital health · 2026Article
- Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation.Scientific reports · 2026Article
- Influence of Anatomical Definition and Substrate Condition on Simulations of Atrial Arrhythmias.Annals of biomedical engineering · 2026Article
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Atrial fibrillation type-specific prediction of recurrence after catheter ablation: the pivotal role of right atrial remodeling revealed by explainable machine learning.Frontiers in cardiovascular medicine · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- Diabetic Atrial Cardiomyopathy: Pathogenesis, Diagnosis, Management, AI-Driven Diagnosis, and Risk Prediction.Journal of diabetes research · 2026Review
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.Communications medicine · 2025Article
- Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence-Driven Analysis of the Electrophysiological Complexity.Journal of cardiovascular electrophysiology · 2025Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- 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
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Medical Digital Twin: A Review on Technical Principles and Clinical Applications.Journal of clinical medicine · 2025Review
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI.
methodsThis single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI.
resultsIn our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47).
conclusionsML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.
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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.