Evidence map›Paper›PMID 41767828›Full record

ArticleEuropean heart journal open2026

Accurate estimation of lesion metrics in radiofrequency ablation using machine learning model.

Masateru Takigawa, Shuji Tsunoda, Takatoshi Shigeta, Junji Yamaguchi, Miho Negishi, Masaki Honda, Ryo Tateishi, Iwanari Kawamura, Tasuku Yamamoto, Kentaro Goto and 6 more

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In one paragraph

Article in European heart journal open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Masateru TakigawaDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0000-0002-8473-7514
Shuji TsunodaDepartment of Materials Process Engineering, University of Nagoya, Chikusa-ku, Nagoya 464-8603, Japan.
Takatoshi ShigetaORCID https://orcid.org/0000-0003-1781-9218
Junji YamaguchiDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Miho NegishiDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0009-0004-4791-2864
Masaki HondaDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0000-0003-1247-8442
Ryo TateishiDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Iwanari KawamuraDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Tasuku YamamotoDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0000-0002-1210-7126
Kentaro GotoDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Takuro NishimuraDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Kazuya YamaoDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0000-0002-4367-078X
Susumu TaoDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Shinsuke MiyazakiDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Koichi FujiwawraDivision of Advanced Arrhythmia Research, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Tetsuo SasanoDepartment of Cardiovascular Medicine, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.ORCID https://orcid.org/0000-0003-3582-6104

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Conventional parameters for estimating lesions in radiofrequency (RF) catheter ablation (RFCA), such as ablation energy (AE), contact force (CF), and impedance variation, often yield suboptimal results. This study aimed to develop a machine learning (ML) model to improve the accuracy of lesion metric estimation in RFCA. Methods and results: RF energies (30-50W) were applied to excised ventricular myocardium using RFCA with CFs of 10  or 20 g for durations between 10 and 180 s, with various orientations. Correlations between total AE, force-time integral, impedance-drop, and lesion metrics were evaluated and compared to ML model predictions, using eXtreme Gradient Boosting (XGBoost). The dataset was split for training (75%) and validation (25%). Feature importance for each lesion metric was also assessed. A total of 1142 ablations were analysed. Total AE had the strongest correlation with max depth, max length, and volume ( Conclusion: ML using multiple RFCA parameters improves lesion metric predictions, enhancing lesion estimation beyond conventional metrics, potentially improving procedural guidance and safety.

Indexed as

BiophysicsCatheter ablationeXtreme Gradient BoostingMachine learningRadiofrequency

Identifiers

PMID41767828
PMCPMC12941214

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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.