Neonatology · 2026
Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data.
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Hwang Jae Kyoon, Jung Donggoo, Park Hyun-Kyung, Kim Daehyun, Do Hyun Jeong, Oh Seong Hee, Kim Seung Hyun, Kim Tae Hyun, Jin Hyunseung
Аннотация
UNLABELLED: <p>Introduction: Retinopathy of prematurity (ROP) remains a leading cause of preventable blindness in preterm infants. This study aimed to develop machine learning (ML) models using non-imaging clinical data to predict ROP, severe ROP (sROP), and treated ROP (tROP) in very low birth weight (VLBW) infants.
METHODS: We utilized nationwide clinical data from the Korean Neonatal Network, including 44 perinatal and neonatal variables. Two deep learning models, Multilayer Perceptron (MLP) and Neural Oblivious Decision Ensembles (NODE), optimized for tabular data, were applied. Additionally, we developed simplified models using eight key variables selected through clinical and algorithmic relevance.
RESULTS: MLP and NODE models demonstrated high predictive performance. For the full 44-variable models, the area under the receiver operating characteristic curve (AUROC) was as follows: ROP (0.853/0.855), sROP (0.888/0.890), and tROP (0.905/0.909). The reduced 8-variable models yielded comparable AUROCs: ROP (0.851/0.855), sROP (0.895/0.895), and tROP (0.910/0.909).
CONCLUSION: 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. </p>.
METHODS: We utilized nationwide clinical data from the Korean Neonatal Network, including 44 perinatal and neonatal variables. Two deep learning models, Multilayer Perceptron (MLP) and Neural Oblivious Decision Ensembles (NODE), optimized for tabular data, were applied. Additionally, we developed simplified models using eight key variables selected through clinical and algorithmic relevance.
RESULTS: MLP and NODE models demonstrated high predictive performance. For the full 44-variable models, the area under the receiver operating characteristic curve (AUROC) was as follows: ROP (0.853/0.855), sROP (0.888/0.890), and tROP (0.905/0.909). The reduced 8-variable models yielded comparable AUROCs: ROP (0.851/0.855), sROP (0.895/0.895), and tROP (0.910/0.909).
CONCLUSION: 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. </p>.