Deep Learning‐Based Prediction Model for Cardiac Resynchronization Therapy Responders Using Electrocardiogram Data
AI models using preimplantation ECG data, particularly those based on image inputs, can effectively predict CRT responders. This approach may enhance patient selection and support personalized therapy strategies in CRT management.
Authors
- Ritsushi Kato (ORCID: https://orcid.org/0000-0002-9989-8708)
- Yasumasa Fujisaki (ORCID: https://orcid.org/0000-0003-1529-1093)
- Tsukasa Naganuma
- Wataru Sasaki (ORCID: https://orcid.org/0000-0003-1418-6566)
- Syunta Higuchi
- Haruka Yamazaki (ORCID: https://orcid.org/0000-0002-1298-1606)
- Daisuke Kawano (ORCID: https://orcid.org/0000-0002-3347-6772)
- Hiroki Yamazaki (ORCID: https://orcid.org/0000-0001-7788-392X)
- Masataka Narita (ORCID: https://orcid.org/0009-0008-0624-9639)
- Naomichi Tanaka
- Toshiki Takeda
- 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
- Wataru Yoshino
Institutions
- Saitama International Medical Center (JP)
- Meguro Parasitological Museum (JP)
- Saitama Medical University (JP)
Publication Details
- Journal
- Journal of Cardiovascular Electrophysiology
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1111/jce.70212
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00