Rapid and accurate prediction of cycloplegic refraction in Chinese children: development and validation of machine learning models.

ABSTRACT:

Background

Uncorrected refractive error affects approximately 19 million children globally, resulting in preventable vision loss. However, cycloplegic refraction, the gold standard for assessment, remains largely inaccessible in low-resource settings. We aimed to develop and validate machine learning (ML) prediction models based on non-cycloplegic results to estimate the cycloplegic spherical equivalent (cSE).

Methods

The internal dataset comprised refractive measurements and ocular biometric parameters collected from 3035 children's eyes at the Eye & ENT Hospital of Fudan University, the research team's primary hospital. The external validation sets consisted of 160 and 120 eyes, respectively, from two different centres. Based on ocular biometric parameters and non-cyclople

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12532443 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature