Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study
The present new deep learning-based method of screening for abnormal electrocardiograms in children showed at least a similar diagnostic performance compared to that of a conventional algorithm. Further studies are warranted to develop an automated analysis of electrocardiograms in school-age children.
Authors
- Shuhei Toba (ORCID: https://orcid.org/0000-0002-2843-1086)
- Hirofumi Sawada (ORCID: https://orcid.org/0000-0001-8069-5751)
- Kazunobu Ohya
- Yuki Nakayama (ORCID: https://orcid.org/0000-0003-2825-2480)
- Yoshihide Mitani (ORCID: https://orcid.org/0000-0002-6038-8161)
- Hisato Ito (ORCID: https://orcid.org/0000-0003-3792-1615)
- Noriko Yodoya
- Takato Yamasaki (ORCID: https://orcid.org/0000-0002-8692-5033)
- Naoki Tsuboya (ORCID: https://orcid.org/0000-0001-8190-3874)
- Masahiro Hirayama (ORCID: https://orcid.org/0000-0002-6850-2782)
- Yusuke Sugitani
- Motoshi Takao (ORCID: https://orcid.org/0000-0003-1305-8554)
- Mami Takeoka (ORCID: https://orcid.org/0000-0002-9849-9828)
- Hiroyuki Ohashi (ORCID: https://orcid.org/0000-0001-6542-7222)
Institutions
- Mie University (JP)
- Mie University Hospital (JP)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-03-05
- DOI
- https://doi.org/10.3389/fcvm.2025.1471989
- Citations
- 2
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 2.78