Machine Learning Risk Prediction for Treated Retinopathy of Prematurity in Infants.

Area of Science:

• Neonatal ophthalmology
• Medical artificial intelligence
• Predictive analytics in healthcare

Background:

• Retinopathy of prematurity (ROP) is a primary cause of childhood blindness.
• Current ROP screening guidelines may be too broad, leading to unnecessary evaluations.
• There is a need for improved models to identify infants at high risk for ROP.

Purpose of the Study:

• To develop and validate machine learning (ML) models for predicting the need for ROP treatment.
• To stratify infants based on ROP treatment timing using ML models.
• To compare the performance of ML models against traditional logistic regression (LR) models.

Main Methods:

• Utilized a multicenter cohort of 103,701 infants (birth weight ≤1,500g or gestational age ≤30 weeks).
• Developed ML models at 2-week intervals from postnatal day 14 to 98 using clinically relevant variables.
• Validated models in a separate cohort of 25,105 infants and compared performance to an LR model.

Main Results:

• The day 28 ML model demonstrated superior performance over the LR model in the validation cohort (AUROC: 0.916 vs. 0.903; AP: 0.190 vs. 0.160).
• At a 100% sensitivity threshold, the ML model achieved a negative predictive value of >99.9%.
• The ML model could potentially reduce the number of infants requiring screening by 14% compared to current guidelines.

Conclusions:

• ML models are effective in predicting the need for ROP treatment and stratifying infant risk.
• These models show potential for reducing unnecessary ROP screenings.
• Further research is required to implement these model-based ROP predictions in clinical practice.