ScholarWorks@성균관대학교

초록

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. Retinopathy of prematurity (ROP) is an eye condition that can affect premature babies (babies born too early, before 37 weeks of pregnancy). In ROP, abnormal blood vessels grow in the retina, which can lead to vision problems or even blindness. To prevent serious outcomes, early detection and treatment are essential. However, not all hospitals have enough trained eye specialists to screen every baby at risk. For this reason, this study aimed to develop an easier way to identify babies who may need eye examinations using commonly collected medical data. To address this goal, the researchers analyzed health records of premature babies collected across South Korea. Using a method called machine learning, which allows computers to find patterns in data, they created two computer models. These models could predict which babies were more likely to develop severe forms of ROP or need treatment. Importantly, the models used only basic clinical information like birth weight, oxygen support, and medical complications, without requiring eye images. The models showed high accuracy even when using just a few key factors. By identifying risk in this way, this type of model can help hospitals recognize high-risk babies early and refer them for specialized care, even if eye doctors are not available on site. It offers a practical, low-cost tool for improving ROP screening programs, especially in areas with limited resources.

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