Comparison of the characteristics between machine learning and deep learning algorithms for ablation site classification in a novel cloud-based system
The R14 model significantly improved sensitivity and PPV compared with the R12.1 model. The tendency for predicting potential reconnection sites was similar to that of the previous version, the R12 model.
Authors
- Kazuo Matsumoto (ORCID: https://orcid.org/0000-0001-8131-7736)
- Ritsushi Kato (ORCID: https://orcid.org/0000-0002-9989-8708)
- Tsukasa Naganuma
- Wataru Sasaki (ORCID: https://orcid.org/0000-0003-1418-6566)
- Daisuke Kawano (ORCID: https://orcid.org/0000-0002-3347-6772)
- Masataka Narita (ORCID: https://orcid.org/0009-0008-0624-9639)
- Naomichi Tanaka
- Kazuhisa Matsumoto (ORCID: https://orcid.org/0000-0002-5648-8135)
- Yoshifumi Ikeda (ORCID: https://orcid.org/0000-0001-9857-9281)
- Hitoshi Mori (ORCID: https://orcid.org/0000-0002-1172-2995)
- Kazuhiko Kuinose
Institutions
- Saitama International Medical Center (JP)
- Saitama Medical University (JP)
Publication Details
- Journal
- Heart Rhythm
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1016/j.hrthm.2025.03.1955
- Citations
- 1
- Primary Topic
- Cardiac Arrhythmias and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Saitama Medical University