Observational studyBMC medical imaging2021
A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.
Observational study in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Three-Dimensional Left Atrial Geometry in Atrial Fibrillation: Imaging Biomarkers, Substrate Phenotyping, and Ablation Outcome Prediction.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors.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 · 2026Article
- Automated analysis of electrogram characteristics in baseline rhythm can distinguish isthmus regions and segments in reentrant ventricular tachycardia.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- Novel predictors of late recurrence after catheter ablation for atrial fibrillation: from biomarkers to artificial intelligence models.Frontiers in cardiovascular medicine · 2026Review
- A computed tomography-based radiomics-clinical model incorporating left atrial and proximal pulmonary vein features predicts recurrence after radiofrequency catheter ablation of atrial fibrillation: a multicenter study.Frontiers in medicine · 2026Article
- Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.Communications medicine · 2025Article
- Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.Computational and structural biotechnology journal · 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
- Artificial Intelligence-Based Feature Analysis of Pulmonary Vein Morphology on Computed Tomography Scans and Risk of Atrial Fibrillation Recurrence After Catheter Ablation: A Multi-Site Study.Circulation. Arrhythmia and electrophysiology · 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
- Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients.Medicina (Kaunas, Lithuania) · 2024Review
- Uplift modeling to identify patients who require extensive catheter ablation procedures among patients with persistent atrial fibrillation.Scientific reports · 2024Article
- Using machine learning to enhance prediction of atrial fibrillation recurrence after catheter ablation.Journal of arrhythmia · 2023Article
- Value of Imaging in the Non-Invasive Prediction of Recurrence after Catheter Ablation in Patients with Atrial Fibrillation: An Up-to-Date Review.Reviews in cardiovascular medicine · 2023Review
- The right modality for the right pulmonary vein shape in ablation for atrial fibrillation.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2023Article
- Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.
methodsThis retrospective study included 68 AF patients who had pre-catheter ablation cardiac CT scans with contrast. AF recurrence was defined at 1 year, excluding a 3-month post-ablation blanking period. After creating atlases of atrial models from segmented AF+ and AF- CT images, an atlas-based implicit shape differentiation method was used to identify surface of interest (SOI). After registering the SOI to each patient model, statistics of the deformation on the SOI were used to create shape descriptors. The performance in predicting AF recurrence using shape features at and outside the SOI and eight clinical factors (age, sex, left atrial volume, left ventricular ejection fraction, body mass index, sinus rhythm, and AF type [persistent vs paroxysmal], catheter-ablation type [Cryoablation vs Irrigated RF]) were compared using 100 runs of fivefold cross validation.
resultsDifferences in atrial shape were found surrounding the pulmonary vein ostia and the base of the left atrial appendage. In the prediction of AF recurrence, the area under the receiver-operating characteristics curve (AUC) was 0.67 for shape features from the SOI, 0.58 for shape features outside the SOI, 0.71 for the clinical parameters, and 0.78 combining shape and clinical features.
conclusionDifferences in left atrial shape were identified between AF recurrent and non-recurrent patients using pre-procedure CT scans. New radiomic features corresponding to the differences in shape were found to predict post-ablation AF recurrence.
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