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Myopia Prediction Using Machine Learning: An External Validation Study.

Rajat S Chandra1, Bole Ying2, Jianyong Wang3

  • 1Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Vision (Basel, Switzerland)
|October 24, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict cycloplegic refractive error and myopia in students using non-cycloplegic data. This external validation confirms ML

Keywords:
cycloplegic refractionmachine learningmyopiapredictionvalidation

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Machine learning (ML) models were previously developed to predict cycloplegic spherical equivalent refraction (SER) and myopia using non-cycloplegic data under standardized conditions.
  • The generalizability of these ML models to diverse clinical settings with varying cycloplegia agents and biometry devices was uncertain.

Purpose of the Study:

  • To evaluate the performance of ML models in predicting cycloplegic SER and myopia status in an independent cohort of Chinese students.
  • To assess the models' robustness despite variations in cycloplegic agents (tropicamide, cyclopentolate) and biometry devices (IOLMaster 700, SW-9000).

Main Methods:

  • An independent cohort of 614 Chinese students aged 8-13 years was studied.
  • Autorefraction was performed before and after cycloplegia using either 0.5% tropicamide or 1% cyclopentolate.
  • Biometric measures were collected using either an IOLMaster 700 or an Optical Biometer SW-9000.
  • ML models (XGBoost, random forest) were evaluated using R², mean absolute error (MAE), sensitivity, specificity, and area under the ROC curve (AUC).

Main Results:

  • The XGBoost model demonstrated excellent prediction of cycloplegic SER (R² = 0.95, MAE = 0.32 D).
  • Both ML models accurately predicted myopia status (random forest: AUC 0.99; XGBoost: AUC not specified) and myopia prevalence (observed 62.9%; predicted 58.8%-60.6%).
  • High sensitivity and specificity were achieved for myopia prediction by both models, even with heterogeneous cycloplegia and biometry data.

Conclusions:

  • XGBoost and random forest ML models performed effectively in predicting cycloplegic SER and myopia status using non-cycloplegic data in an independent cohort.
  • External validation confirmed that ML models can reliably estimate cycloplegic SER and myopia prevalence despite heterogeneous clinical parameters.
  • Further studies in diverse populations are recommended to solidify the utility of ML tools for ophthalmic assessments.