Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram
- DOI
- 10.1016/j.ijcrp.2025.200557
- Published
- 2026-03
- Container
- International Journal of Cardiology Cardiovascular Risk and Prevention
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ijcrp.2025.200557,
title = {Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram},
author = {Chun-Chin Chang and Ming-Tsung Hsieh and Yin-Hao Lee and Chih-Hsueh Tseng and Wei-Ming Huang and Ruey-Hsing Chou and Chin-Sheng Lin and Po-Hsun Huang},
year = {2026},
journal = {International Journal of Cardiology Cardiovascular Risk and Prevention},
doi = {10.1016/j.ijcrp.2025.200557},
url = {https://doi.org/10.1016/j.ijcrp.2025.200557}
}RIS
TY - JOUR TI - Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram AU - Chun-Chin Chang AU - Ming-Tsung Hsieh AU - Yin-Hao Lee AU - Chih-Hsueh Tseng AU - Wei-Ming Huang AU - Ruey-Hsing Chou AU - Chin-Sheng Lin AU - Po-Hsun Huang PY - 2026 JO - International Journal of Cardiology Cardiovascular Risk and Prevention DO - 10.1016/j.ijcrp.2025.200557 UR - https://doi.org/10.1016/j.ijcrp.2025.200557 ER -
APA
Chang, C., Hsieh, M., Lee, Y., Tseng, C., Huang, W., Chou, R., Lin, C., & Huang, P. (2026). Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram. International Journal of Cardiology Cardiovascular Risk and Prevention. https://doi.org/10.1016/j.ijcrp.2025.200557
Source records
- crossref · retrieved 2026-09-25T20:33:49.043Z