ArticleJournal of healthcare engineering2022
Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled 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
- Accurate estimation of lesion metrics in radiofrequency ablation using machine learning model.European heart journal open · 2026Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 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
- 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
- Postoperative recurrence prediction model for atrial fibrillation: a meta-analysis.American journal of translational research · 2024Review
- A machine learning approach to predict the glaucoma filtration surgery outcome.Scientific reports · 2023Article
- The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.Reviews in cardiovascular medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the features affecting prognosis remains to be developed. In this paper, we propose a deep learning model for predicting the late recurrence after RFCA in patients with AF. Aiming to predict the late recurrence (LR) of AF within 1 year after pulmonary vein isolation, we designed a multimodal model based on the multilayer perceptron architecture. For quantitative evaluation, we conducted 4-fold cross-validation on data from 177 AF patients including 47 LR patients. The proposed model (area under the receiver operating characteristic curve-AUROC, 0.766) outperformed the acute patient physiologic and laboratory evaluation (APPLE) score (AUROC, 0.605), CHA
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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.