Deep Learning Prediction of Childhood Myopia Progression Using Fundus Image and Refraction Data

Can deep learning methods accurately predict childhood myopia progression using only fundus images and baseline refraction data?In this cohort study of 3048 schoolchildren with 16 211 fundus images, a novel deep learning model achieved high accuracy in predicting myopia and high myopia risk as well as a mean absolute error of 0.322 D per year for refraction.This deep learning approach enables accurate prediction of myopia progression without extensive metadata and may facilitate large-scale screening and early-intervention efforts in resource-limited settings.

Corresponding Authors: Shi-Ming Li, MD, PhD, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases, Capital Medical University, Beijing, China, 100730 (lishiming81@163.com); Shuo Gao, PhD, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China, 100191 (shuo_gao@buaa.edu.cn).

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