From Eye Examination to Early Cognitive Evaluation for Preterm Newborns: An Explainable Machine Learning Approach

Purpose To use machine learning (ML) to identify the important retinal features associated with cognitive development in newborns with prematurity in early childhood, with the goal of screening preterm babies with poor neurodevelopment for early intervention. Methods We retrospectively reviewed the medical charts of 163 infants (326 eyes) born at Chang Gung Memorial Hospital between 2011 and 2024 who underwent IQ testing and scored < 85 or ≥ 115. Essential characteristics considered predictors come from perinatal variables and eye examination for retinopathy of prematurity (ROP) with optical coherence tomography (OCT) images. The ML model was trained using Random Forest (RF) to collectively associate the predictors with the cognitive assessment outcome. SHAP (Shapley Additive exPlanations) was used to identify the clinical features most predictive of the mental development of a preterm baby. Results The overall accuracy of the ML model was 83.44%, combining all the clinical characteristics, including retinal thickness and retinal thickness difference in bilateral eyes obtained from OCT. The SHAP analysis reveals that lower birth weight, higher stage of ROP, and lower zone development are highly associated with lower IQ scores in our study cohort. Conclusions Through an ML approach, this study identified the BW and the stage of ROP and zone as the leading ocular features associated with cognitive outcomes in preterm newborns in early childhood. It paved the way for timely interventions to improve long-term neurodevelopmental outcomes.