ArticleJournal of arrhythmia2024
Validation of ablation site classification accuracy and trends in the prediction of potential reconnection sites for atrial fibrillation using the CARTONET® R12.1 model.
Article in Journal of arrhythmia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Interlesion Time as a Key Metric of Procedural Proficiency in Atrial Fibrillation Ablation: From Fellow to Attending.Journal of arrhythmia · 2026Article
- Segmental analysis of left atrial substrate severity predictors in patients undergoing persistent atrial fibrillation ablation.Frontiers in cardiovascular medicine · 2026Article
- Morphology-based ECG criteria for identifying left-atrial low-voltage areas on voltage mapping: a diagnostic accuracy study.Frontiers in cardiovascular medicine · 2026Article
- Validation of ablation site classification accuracy and trends in the prediction of potential reconnection sites for atrial fibrillation using the CARTONET® R12.1 model.Journal of arrhythmia · 2024Article
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12 authors.
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Abstract
Background: CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods: We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non-PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results: A total of 29,422 points were analyzed (PV lesions [ Conclusion: The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites.
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