Introduction:Cycloplegic refraction is the gold standard for assessing refractive error in children.However,logistical constraints hinder its implementation in large-scale surveys.Methods:Data obtained from a nationwi...Introduction:Cycloplegic refraction is the gold standard for assessing refractive error in children.However,logistical constraints hinder its implementation in large-scale surveys.Methods:Data obtained from a nationwide ocular health survey conducted in ten provincial-level administrative divisions in China were analyzed(2020–2024).Participants aged 5–18 years underwent standardized non-cycloplegic and cycloplegic autorefraction,axial length(AL),corneal radius(CR),and AL/CR measurements.Random forest and XGBoost models were trained to predict the cycloplegic spherical equivalent(SE)using noncycloplegic SE,uncorrected visual acuity(UCVA),and biometric parameters.Performance was evaluated using R^(2),root mean square error(RMSE),and Bland–Altman analysis.Results:Both models exhibited strong predictive performance.In the test set,random forest achieved R^(2)=0.88 and RMSE=0.55 diopter(D),whereas XGBoost achieved R^(2)=0.89 and RMSE=0.54 D.Noncycloplegic SE,AL/CR ratio,AL,and UCVA were consistently the top predictors.The predicted SE exhibited strong agreement with the cycloplegic SE,with minimal residual bias.Conclusion:Machine learning models incorporating noncycloplegic SE and ocular biometrics accurately estimate cycloplegic SE in children and adolescents,providing a practical alternative for largescale refractive-error surveillance when cycloplegia is impractical.展开更多
文摘Introduction:Cycloplegic refraction is the gold standard for assessing refractive error in children.However,logistical constraints hinder its implementation in large-scale surveys.Methods:Data obtained from a nationwide ocular health survey conducted in ten provincial-level administrative divisions in China were analyzed(2020–2024).Participants aged 5–18 years underwent standardized non-cycloplegic and cycloplegic autorefraction,axial length(AL),corneal radius(CR),and AL/CR measurements.Random forest and XGBoost models were trained to predict the cycloplegic spherical equivalent(SE)using noncycloplegic SE,uncorrected visual acuity(UCVA),and biometric parameters.Performance was evaluated using R^(2),root mean square error(RMSE),and Bland–Altman analysis.Results:Both models exhibited strong predictive performance.In the test set,random forest achieved R^(2)=0.88 and RMSE=0.55 diopter(D),whereas XGBoost achieved R^(2)=0.89 and RMSE=0.54 D.Noncycloplegic SE,AL/CR ratio,AL,and UCVA were consistently the top predictors.The predicted SE exhibited strong agreement with the cycloplegic SE,with minimal residual bias.Conclusion:Machine learning models incorporating noncycloplegic SE and ocular biometrics accurately estimate cycloplegic SE in children and adolescents,providing a practical alternative for largescale refractive-error surveillance when cycloplegia is impractical.
