{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:15:25Z","timestamp":1783318525028,"version":"3.54.6"},"reference-count":11,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fcvm.2026.1859430","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:38:37Z","timestamp":1783316317000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning model to generate patient-specific pulmonary vein isolation lines from successful atrial fibrillation ablation cases: a proof-of-concept study"],"prefix":"10.3389","volume":"13","author":[{"given":"Kazuo","family":"Sakamoto","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Medicine\/Coronary Care Unit, Kyushu University Hospital","place":["Fukuoka, 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