Artificial intelligence for pediatric height prediction using large-scale longitudinal body composition data
Our model produces accurate and personalized growth curves, incorporating explainable AI techniques for enhanced clinical understanding. This method advances pediatric growth assessment and provides robust clinical decision support. Despite limitations including the absence of handwrist radiography comparison and Korean population specificity, our approach demonstrates significant potential for early identification of growth disorders and optimization of growth outcomes.
Authors
- Hae Woon Jung (ORCID: https://orcid.org/0000-0003-0494-4626)
- Woo Young Jang (ORCID: https://orcid.org/0000-0003-1775-7715)
- Jihun Kim (ORCID: https://orcid.org/0000-0002-2957-8776)
- Jong-Ho Kang (ORCID: https://orcid.org/0000-0002-4273-2440)
- Dohyun Chun (ORCID: https://orcid.org/0000-0003-3031-4011)
Institutions
- Chonnam National University (KR)
- Kangwon National University (KR)
- Yonsei University (KR)
- Korea University (KR)
- KG Chemical (South Korea) (KR)
- Kyung Hee University Medical Center (KR)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251395975
- Citations
- 2
- Primary Topic
- Body Composition Measurement Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.33