AI-driven voltage map analysis for optimizing catheter ablation strategy in atrial fibrillation: a proof-of-concept study

Aims: Pulmonary vein isolation (PVI) has been established as the standard catheter ablation (CA) strategy for atrial fibrillation (AF). However, approximately 20-40% of patients experience recurrence after CA. Although three-dimensional (3D) maps generated during CA provide valuable electrophysiological information, they may not be fully utilized in clinical decision-making. To develop an artificial intelligence (AI) model that analyses 3D voltage maps and long-term AF recurrence to guide best practices in CA for AF. Methods and results: < 0.001). Conclusion: The developed AI model predicts AF recurrence based on the completion of PVI and be-PVI and accurately identifies patients who may require further intervention. AI analysis of intraoperative 3D maps could guide optimal CA strategy planning, considering long-term AF recurrence.

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Publication Details

Journal
European Heart Journal - Digital Health
Published
2026-03-31
DOI
https://doi.org/10.1093/ehjdh/ztag054
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
Field-Weighted Citation Impact
0.00

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article

AI-driven voltage map analysis for optimizing catheter ablation strategy in atrial fibrillation: a proof-of-concept study

Kei Inoue, Kazuhiro Nagaoka, Hirotake Yokoyama, Takeshi Tohyama et al.
European Heart Journal - Digital Health
Atrial Fibrillation Management and Outcomes
article

AI-driven voltage map analysis for optimizing catheter ablation strategy in atrial fibrillation: a proof-of-concept study

Kei Inoue, Kazuhiro Nagaoka, Hirotake Yokoyama, Takeshi Tohyama, Yasushi Mukai, Shujiro Inoue, K Todaka, Kazuo Sakamoto, Daisuke Yakabe, Tomomi Nagayama, Shunsuke Kawai, Mitsutaka Yamamoto, Kiyohiro Ogawa, K Abe, Susumu Takase, Takeshi Mikami, Kazuya Hosokawa, Hiroyuki Tsutsui, Atsushi Tanaka
article en

Abstract

Aims: Pulmonary vein isolation (PVI) has been established as the standard catheter ablation (CA) strategy for atrial fibrillation (AF). However, approximately 20-40% of patients experience recurrence after CA. Although three-dimensional (3D) maps generated during CA provide valuable electrophysiological information, they may not be fully utilized in clinical decision-making. To develop an artificial intelligence (AI) model that analyses 3D voltage maps and long-term AF recurrence to guide best practices in CA for AF. Methods and results: < 0.001). Conclusion: The developed AI model predicts AF recurrence based on the completion of PVI and be-PVI and accurately identifies patients who may require further intervention. AI analysis of intraoperative 3D maps could guide optimal CA strategy planning, considering long-term AF recurrence.

European Heart Journal - Digital HealthVol. 7(4)
Kyushu University (JP), University of Teacher Education Fukuoka (JP), National Kyushu Medical Center (JP), Fukuoka International University (JP), St Mary's Hospital (JP), Mitsui Sugar (Japan) (JP), Kyushu University Hospital (JP), Hamanomachi Hospital (JP), Japanese Red Cross Fukuoka Hospital (JP), Harasanshin Hospital (JP), Aso Iizuka Hospital (JP), Saiseikai Fukuoka General Hospital (JP), Fukuoka City Hospital (JP), Massachusetts Institute of Technology (US)
Japan Agency for Medical Research and Development, Fukuda Foundation for Medical Technology, Japan Society for the Promotion of Science
Peace, Justice and strong institutions
Openalex Percentile: Top 34%
Atrial Fibrillation Management and Outcomes
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