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August 9, 2024Translational Vision Science & TechnologyOpen Access

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students

Bole Ying Rajat S. Chandra Jianyong Wang Hongguang Cui Julius T. Oatts

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Abstract

To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

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Cite This Study

Ying et al. (2024) studied this question.

synapsesocial.com/papers/68e5cdb1b6db643587563a54 https://doi.org/10.1167/tvst.13.8.16

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Machine learning classification of cycloplegic refractive status in children and adolescents using non-cycloplegic data across ten provincial-level administrative divisionsOriginal Research · 2026
2Rapid and accurate prediction of cycloplegic refraction in Chinese children: development and validation of machine learning models2025 · 2 citations
3School-level prediction and management of myopia in children and adolescents2025 · 6 citations
4Optimizing myopia prediction in children and adolescents using machine learning: a multi-factorial risk assessment model2025
5Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rateRetrospective Study · 2026

More related papers

Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm2025
Development of a concise objective biometric triage model for identifying myopia defined by cycloplegic refraction in school-aged children2026
Predictive modeling of cycloplegic refraction using non-cycloplegia ocular parameters with emphasis on lens-related features2024

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