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Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
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A Predictive Model for Personalized Fitting of Peripheral Defocus Spectacles Based on Ocular Biometric Parameters.

Peng Zhou1,2, Yingli Li1,3,4,5,6, Sitong Chen1,3,4,5,6

  • 1Optometry Center, Peking University People's Hospital, Beijing, 100034, People's Republic of China.

Clinical Optometry
|March 23, 2026
PubMed
Summary

This study developed predictive models for personalized myopia control using peripheral defocus spectacles. The models accurately predict spectacle effectiveness based on individual ocular biometric parameters for better clinical recommendations.

Keywords:
defocus spectacle lensesmyopia controlocular biometrypersonalized selectionpredictive model

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Area of Science:

  • Ophthalmology
  • Optometry
  • Biomedical Engineering

Background:

  • Myopia is a growing public health concern, particularly in children.
  • Peripheral defocus spectacles are a common intervention for myopia control.
  • Personalized spectacle selection can optimize treatment efficacy.

Purpose of the Study:

  • To develop predictive models for personalized recommendation of peripheral defocus spectacle designs.
  • To base these recommendations on individual ocular biometric parameters.

Main Methods:

  • A retrospective cohort study of 2824 myopic children using five different peripheral defocus spectacle designs.
  • Development of multiple linear regression models using ocular biometric data.
  • Model validation using training (80%) and validation (20%) datasets.

Main Results:

  • No statistically significant difference in axial elongation control among the five designs.
  • High predictive accuracy (R-squared 0.88-0.97) and low error (RMSE 0.04-0.07 mm) for the developed models.
  • Good agreement between predicted and observed values shown by calibration plots.

Conclusions:

  • Predictive models based on ocular biometric parameters can guide personalized fitting of peripheral defocus spectacles.
  • While average efficacy was similar across designs, personalization enhances clinical decision-making.