A graph neural network trained on simulated body surface potential maps to predict atrial fibrillation ablation success

Background Success rates for atrial fibrillation (AF) catheter ablation remain suboptimal, with outcomes typically around 70% for paroxysmal AF and barely exceeding 50% for persistent AF, highlighting a critical need for tools that can non-invasively predict patient-specific outcomes to guide therapy. We hypothesized that a graph neural network (GNN), trained on simulated data, could learn to identify complex patterns in pre-procedural body surface potential maps (BSPMs) that are predictive of success after pulmonary vein and posterior wall isolation (PVI+PW). Purpose This study aims to develop a GNN model capable of predicting the success of PVI + PW ablation using non-invasive input data derived from BSPM (Figure 1). Methods A GNN was developed and trained exclusively on a large dataset of 13,447 in-silico (simulated) AF episodes. Ablation "success" for training was defined as arrhythmia termination following a virtual PVI+PW procedure. The GNN model's input consists of pre-ablation BSPMs, where torso electrodes are treated as nodes in a graph to capture complex spatiotemporal relationships. A proof-of-concept validation was then performed in a cohort of 12 patients undergoing PVI+PW ablation. The model generated a success score between 0 and 1 per patient, using 0.5 as the threshold to predict benefit from PVI+PW ablation, and was compared against the 6-month clinical outcome (arrhythmia recurrence) (Figure 2). Results At 6-month follow-up, 7 of the 12 patients (58%) were free from arrhythmia (success group), while 5 (42%) had recurrence (recurrence group). The GNN model correctly predicted the successful outcome (Figure 2) in 6 of the 7 patients in the success group (sensitivity: 86%). Conversely, it correctly predicted recurrence in only 2 of the 5 patients in the recurrence group (specificity: 40%). This resulted in an overall accuracy of 67% (8 out of 12 patients correctly classified). Conclusion We demonstrate the feasibility of a novel approach: training a GNN entirely on in-silico data to predict clinical AF ablation outcomes from non-invasive BSPMs. The model shows a strong ability to identify patients who will likely benefit from PVI+PW ablation. While validation in larger cohorts is required to improve specificity, this proof-of-concept establishes a powerful novel framework for developing AI-driven tools to improve patient selection for personalize AF ablation therapy.Purpose pictureresults picture