ArticleJournal of cardiovascular electrophysiology2024
Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence.
Article in Journal of cardiovascular electrophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Double potential gradient analysis for critical isthmus detection in scar-related atrial tachycardia.Heart rhythm · 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
- [Atrial tachycardia after previous ablation-right atrial, left atrial, biatrial].Herzschrittmachertherapie & Elektrophysiologie · 2026Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Optimized isochronal late activation methods in the visualization and isthmus identification of ventricular tachycardia.Heart rhythm · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
introductionAblation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses.
methodsElectroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas.
resultsA total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group.
conclusionA GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.
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