Evidence map›Paper›PMID 42077379›Full record

ArticleEuropean heart journal. Digital health2026

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

Takeshi Tohyama, Kazuo Sakamoto, Tomomi Nagayama, Hirotake Yokoyama, Tsukasa Watanabe, Yasushi Mukai, Shunsuke Kawai, Daisuke Yakabe, Hiroshi Mannoji, Kazuhiro Nagaoka and 11 more

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In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Takeshi TohyamaDepartment of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
Kazuo SakamotoDepartment of Cardiovascular Medicine/Coronary Care Unit, Kyushu University Hospital, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Tomomi NagayamaDepartment of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
Hirotake YokoyamaDepartment of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Tsukasa WatanabeDepartment of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.ORCID https://orcid.org/0009-0002-4539-1730
Yasushi MukaiDepartment of Cardiology, Japanese Red Cross Fukuoka Hospital, Fukuoka, Japan.ORCID https://orcid.org/0000-0002-0275-7280
Shunsuke KawaiDepartment of Cardiology, Japanese Red Cross Fukuoka Hospital, Fukuoka, Japan.
Daisuke YakabeDepartment of Cardiovascular Internal Medicine, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan.ORCID https://orcid.org/0000-0003-0204-9505
Hiroshi MannojiDepartment of Cardiovascular Medicine, Hamanomachi Hospital, Fukuoka, Japan.
Kazuhiro NagaokaDepartment of Cardiovascular Medicine, St.Mary's Hospital, Fukuoka, Japan.
Atsushi TanakaDivision of Cardiology, Cardiovascular and Aortic Center, Saiseikai Fukuoka General Hospital, Fukuoka, Japan.
Mitsutaka YamamotoDepartment of Cardiovascular Medicine, Harasanshin Hospital, Fukuoka, Japan.
Kiyohiro OgawaDepartment of Cardiology, Fukuoka City Hospital, Fukuoka, Japan.
Takeshi MikamiDepartment of Cardiology, Munakata Suikokai General Hospital, Fukuoka, Japan.
Shujiro InoueDepartment of Cardiology, Aso Iizuka Hospital, Iizuka, Japan.
Susumu TakaseDepartment of Cardiovascular Medicine, Kyushu University Hospital, Fukuoka, Japan.ORCID https://orcid.org/0000-0002-3929-0071
Kei InoueSUSMED, Inc., Tokyo, Japan.
Kazuya HosokawaDepartment of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
Koji TodakaCentre for Clinical and Translational Research of Kyushu University Hospital, Fukuoka, Japan.
Hiroyuki TsutsuiSchool of Health Sciences, International University of Health and Welfare, Fukuoka, Japan.
Kohtaro AbeDepartment of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.ORCID https://orcid.org/0000-0003-4131-5423

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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: A dedicated multicentre registry recording detailed CA data for AF and recurrence was used to develop the AI model. The model was designed to evaluate the completion of PVI and ablation beyond-PVI (be-PVI), considering future AF recurrence with the need for additional PVI and be-PVI interventions. The AI model was trained and validated via fivefold cross-validation with 1268 maps. It effectively stratified cases for predicting 1-year AF recurrence after CA ( 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.

Indexed as

Artificial intelligenceAtrial fibrillationCatheter ablationElectrophysiological mapMachine learningVoltage map

Identifiers

PMID42077379
PMCPMC13131983

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.