Predicting myopia risk using a machine learning model based on fundus imageomics.

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

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12700998 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature