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Background <p>Cycloplegic refraction remains the gold standard for assessing refractive error in children, yet its application in large-scale screening is limited. Machine learning offers a promising approach for screening and triage by leveraging non-cycloplegic data for refractive classification.</p> Methods <p>This study utilized a multicenter dataset comprising 54,374 participants aged 5–18 years from ten provincial-level administrative divisions (PLADs) across China. We developed and validated four machine learning models (LightGBM, XGBoost, Random Forest, and MLP) and a multinomial logistic regression model as a conventional baseline comparator to classify refractive status (myopia, emmetropia, hyperopia). Model generalizability was evaluated via leave-one-PLAD-out cross-validation. Feature importance, calibration, and age-stratified trend analyses were conducted to assess interpretability and developmental robustness.</p> Results <p>All models achieved excellent discrimination (macro-AUC 0.910–0.913) and maintained stable performance across PLADs (AUC 0.866–0.950). While all ensemble models achieved comparable discrimination (macro-AUC 0.910–0.913), LightGBM demonstrated slightly lower log-loss and more balanced probability calibration. Therefore, it was selected as the primary model for subsequent age-stratified analyses. Key predictive features included non-cycloplegic SE, AL/CR ratio, UCVA, and axial length. Age-stratified evaluation revealed systematic performance improvement from childhood to adolescence, with accuracy increasing from 0.80 (5–8 years) to 0.89 (16–18 years) and AUC peaking at 0.93 (12–15 years). Linear trend analysis confirmed significant age-related enhancement independent of provincial effects.</p> Conclusions <p>LightGBM-based classification of cycloplegic refractive status from non-cycloplegic optical and biometric data provides a robust, generalizable, and clinically interpretable approach for pediatric vision screening. The model is best positioned as a screening and triage support tool, enabling risk stratification and prioritization for confirmatory cycloplegic refraction rather than replacing it, particularly in large-scale or resource-limited settings.</p>

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Machine learning classification of cycloplegic refractive status in children and adolescents using non-cycloplegic data across ten provincial-level administrative divisions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Keke Liu,
  • Huijuan Luo,
  • Yan Chen,
  • Boran E,
  • Huining Kuang,
  • Chenyu Zhang,
  • Xin Guo

摘要

Background

Cycloplegic refraction remains the gold standard for assessing refractive error in children, yet its application in large-scale screening is limited. Machine learning offers a promising approach for screening and triage by leveraging non-cycloplegic data for refractive classification.

Methods

This study utilized a multicenter dataset comprising 54,374 participants aged 5–18 years from ten provincial-level administrative divisions (PLADs) across China. We developed and validated four machine learning models (LightGBM, XGBoost, Random Forest, and MLP) and a multinomial logistic regression model as a conventional baseline comparator to classify refractive status (myopia, emmetropia, hyperopia). Model generalizability was evaluated via leave-one-PLAD-out cross-validation. Feature importance, calibration, and age-stratified trend analyses were conducted to assess interpretability and developmental robustness.

Results

All models achieved excellent discrimination (macro-AUC 0.910–0.913) and maintained stable performance across PLADs (AUC 0.866–0.950). While all ensemble models achieved comparable discrimination (macro-AUC 0.910–0.913), LightGBM demonstrated slightly lower log-loss and more balanced probability calibration. Therefore, it was selected as the primary model for subsequent age-stratified analyses. Key predictive features included non-cycloplegic SE, AL/CR ratio, UCVA, and axial length. Age-stratified evaluation revealed systematic performance improvement from childhood to adolescence, with accuracy increasing from 0.80 (5–8 years) to 0.89 (16–18 years) and AUC peaking at 0.93 (12–15 years). Linear trend analysis confirmed significant age-related enhancement independent of provincial effects.

Conclusions

LightGBM-based classification of cycloplegic refractive status from non-cycloplegic optical and biometric data provides a robust, generalizable, and clinically interpretable approach for pediatric vision screening. The model is best positioned as a screening and triage support tool, enabling risk stratification and prioritization for confirmatory cycloplegic refraction rather than replacing it, particularly in large-scale or resource-limited settings.