ArticleJournal of cardiovascular electrophysiology2023
Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.
Article in Journal of cardiovascular electrophysiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 28 citations in OpenAlex.
- Three-Dimensional Left Atrial Geometry in Atrial Fibrillation: Imaging Biomarkers, Substrate Phenotyping, and Ablation Outcome Prediction.Diagnostics (Basel, Switzerland) · 2026Review
- Circulating microRNAs in Atrial Fibrillation: Clinical Significance and Future Perspectives.Medicina (Kaunas, Lithuania) · 2026Review
- AI-driven voltage map analysis for optimizing catheter ablation strategy in atrial fibrillation: a proof-of-concept study.European heart journal. Digital health · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- Managing Atrial Fibrillation in Heart Failure: In Whom, When, and How?International journal of heart failure · 2026Review
- Accurate estimation of lesion metrics in radiofrequency ablation using machine learning model.European heart journal open · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- Epicardial adipose tissue and ablation outcomes in obese patients with paroxysmal atrial fibrillation: A comparison of pulsed field and radiofrequency ablation.Heart rhythm O2 · 2025Article
- 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
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Role of Computational Modelling in Enhancing Thermal Safety During Cardiac Ablation.Interdisciplinary cardiovascular and thoracic surgery · 2025Review
- Targeting the Substrate: Mechanism-Based Ablation Strategies for Persistent Atrial Fibrillation.Journal of clinical medicine · 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
- Beyond Pulmonary Vein Reconnection: Exploring the Dynamic Pathophysiology of Atrial Fibrillation Recurrence After Catheter Ablation.Journal of clinical medicine · 2025Review
- Prediction of Ablation Index and Lesion Size Index for Local Impedance Drop-Guided Ablation.Journal of clinical medicine · 2025Article
- 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
- Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
- Implementing AI in clinical practice: Practical insights on integrating AI through digital dashboards and beyond.Heart rhythm · 2024Article
- Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation.Journal of cardiovascular development and disease · 2024Review
Corrections and comments
- Commented on by
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundStructural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation.
methodsConsecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification.
resultsThree hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764).
conclusionOur ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.
Indexed as
Identifiers
What OpenQuestion holds
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.