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Related Experiment Video

Updated: May 20, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
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Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Machine learning-driven prediction of cycloplegic refractive error in Chinese children.

Bichi Chen1, Li Tian2, Fuyue Tian2

  • 1Vision X Medical Technology Co., Ltd., Shanghai, China.

Frontiers in Cell and Developmental Biology
|June 6, 2025
PubMed
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Machine learning models accurately predict cycloplegic spherical equivalent refraction (SER) using non-cycloplegic data in children. This offers a practical alternative for pediatric eye exams when cycloplegia is not feasible.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Pediatric ophthalmic assessments often require cycloplegic refraction for accurate refractive error determination.
  • Cycloplegia can be challenging in pediatric populations due to factors like patient cooperation and time constraints.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting cycloplegic spherical equivalent refraction (SER) from non-cycloplegic parameters.
  • To provide a practical alternative for pediatric refractive assessments.

Main Methods:

  • Trained six ML models (linear regression, random forest, XGBoost, MLP, SVM, LGBM) on data from 2,274 Chinese children (ages 3-16).
  • Utilized demographics, non-cycloplegic refractive error, and ocular biometrics for model training.
Keywords:
cycloplegic refractionmachine learningmyopiapredictionrefractive error

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Last Updated: May 20, 2026

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  • Evaluated model performance using R², mean error (ME), mean absolute error (MAE), and clinical accuracy.

    • Main Results:

    • ML models achieved high accuracy in predicting cycloplegic SER (R²: 0.920–0.934, MAE: 0.385–0.413 D).
    • The multilayer perceptron model showed the best performance (R² = 0.934, MAE = 0.385 D), with 73.08% and 94.29% of predictions within ±0.50 D and ±1.00 D.
    • Optimal performance was observed in children aged 7–10 years and those with low myopia.

    Conclusions:

    • Machine learning models can accurately estimate cycloplegic SER using non-cycloplegic measurements.
    • These models offer a viable and practical alternative for pediatric refractive assessments, especially when cycloplegia is not feasible.