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AI / informaticsFrontiers in public health · 2026

Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.

Haoqiang Cui, Jianning Huang, Zhenbao Zhou … Junhua Zhang

OphthalmologyAI / informatics

Machine learning model predicts which children need cycloplegic refraction, not just non-cycloplegic exam

Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.

Haoqiang Cui … Junhua Zhang

Frontiers in public health · 2026

Background

Traditional cycloplegic refraction is the gold standard for pediatric vision screening but is often limited by low efficiency and poor compliance.

Purpose

This study aimed to develop a machine learning model using non-cycloplegic visual function and refractive parameters to evaluate the reliability of non-cycloplegic refraction in children and adolescents.

Methods

A total of 300 children and adolescents (547 eyes) were included.

n = 547 eyes

Results

higher score means the model more accurately flags kids needing dilated exams

AUC 0.87 (95% CI 0.80 to 0.94)

CI excludes the null - significant

More results

An absolute DSE greater than 0.25 D was associated with multiple accommodative and refractive parameters.

A nomogram was constructed to estimate the probability of DSE greater than 0.25 D.

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Conclusion

Machine learning models based on routine non-cycloplegic parameters can effectively identify children who require cycloplegic refraction, providing an interpretable and practical decision-support tool to reduce unnecessary cycloplegia and improve clinical efficiency.

Post

Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.

Machine learning model predicts which children need cycloplegic refraction, not just non-cycloplegic exam

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