Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data
The proposed ML models based on nationwide non-imaging clinical data enable early risk identification and timely intervention for ROP in VLBW infants. This cost-effective and scalable approach may help improve outcomes, especially in resource-limited settings.
Authors
- Hyun-Seung Jin
- Donggoo Jung (ORCID: https://orcid.org/0000-0002-0638-8793)
- Hyun Chul Jeong (ORCID: https://orcid.org/0000-0001-8699-4023)
- Hyun Kyung Park (ORCID: https://orcid.org/0000-0002-4509-4237)
- Seong Hee Oh
- Daehyun Kim
- Tae Hyun Kim
- Seung Hyun Kim
- Jae Kyoon Hwang
Institutions
- Ulsan College (KR)
- Samsung Medical Center (KR)
- University of Ulsan (KR)
- Gangneung Asan Hospital (KR)
- Hanyang University (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Neonatology
- Published
- 2026-01-19
- DOI
- https://doi.org/10.1159/000550513
- Primary Topic
- Retinopathy of Prematurity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hanyang University
- Gangneung Asan Hospital
- Ministry of Science and ICT, South Korea
- National Institutes of Health
- Institute for Information and Communications Technology Promotion