ArticleFrontiers in cardiovascular medicine2026
Deep learning model to generate patient-specific pulmonary vein isolation lines from successful atrial fibrillation ablation cases: a proof-of-concept study.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pulmonary vein isolation (PVI) is an established standard ablation for atrial fibrillation (AF), however, AF recurrence remains a major clinical challenge. We developed a deep learning model to generate patient-specific PVI lines on pre-ablation 3D voltage maps, using lesion sets from successful AF ablation cases with documented freedom from recurrence for more than one year as ground truth. Using a U-Net-based architecture trained and evaluated on 513 maps from 171 such cases, the model reproduced the anatomical and electrophysiological features of these PVI lesion sets. On the held-out test set, the model achieved a mean Intersection over Union of 0.87 and a Dice score of 0.93. As a proof of concept, these findings suggest that the model can reproduce patient-specific PVI patterns associated with successful outcomes; whether this translates into reduced recurrence requires prospective clinical validation.
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