Abstract
The purpose of this study was to develop a machine learning-based model using quantitative color fundus photography (CFP) data to predict myopia risk in school-age children, based on the axial length/corneal curvature radius (AL/CR) ratio, and to identify key retinal features associated with myopia progression. This cross-sectional study included 2,184 CFPs from children aged 6-10 years. Retinal imageomics features were extracted from CFPs using the EVisionAI platform, alongside age and sex data, resulting in 146 variables. After feature selection using LASSO regression and expert review, predictive models were constructed using seven machine learning algorithms, including random forest (RF), XGBoost, and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration. The RF model showed the best predictive performance (AUC = 0.798), followed by LightGBM and XGBoost. Key predictors included age, nasal disc-foveal distance, atrophic areas, and vascular parameters. The RF model demonstrated high specificity (0.80) and moderate sensitivity (0.59), with robust calibration and decision curve analysis confirming its clinical value. This study demonstrates that quantitative CFP-derived imageomics, combined with machine learning, can effectively predict myopia risk in school-age children. The RF model, incorporating age, retinal distances, and vascular features, offers a promising tool for early myopia risk stratification.