Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram
This study highlights the potential of AI-assisted ECG analysis for CHD screening in young children. The ResNet-18-based model outperformed conventional ECG evaluation, suggesting its feasibility as a supplementary tool for early CHD detection. Future studies should focus on multi-center validation, inclusion of more CHD subtypes, and integration with other screening modalities to improve diagnostic accuracy and clinical applicability.
Authors
- Mao‐Sheng Hwang
- Ya‐Ting Chang (ORCID: https://orcid.org/0000-0003-2580-4622)
- Hung‐Tao Chung (ORCID: https://orcid.org/0000-0001-6760-8107)
- Hsin-Mao Hsu (ORCID: https://orcid.org/0000-0002-9233-2314)
- Hao-Chuan Liu (ORCID: https://orcid.org/0000-0003-3119-2204)
- Syu‐Jyun Peng (ORCID: https://orcid.org/0000-0001-5002-6581)
- Yu-Shin Lee
- Jainn‐Jim Lin (ORCID: https://orcid.org/0000-0001-5592-6343)
Institutions
- Chang Gung Memorial Hospital (TW)
- Linkou Chang Gung Memorial Hospital (TW)
- Taipei Medical University (TW)
Publication Details
- Journal
- BMC Pediatrics
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1186/s12887-025-05628-2
- Citations
- 5
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 6.94
Funders
- Taipei Medical University
- National Taipei University of Technology
- National Science and Technology Council