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Related Experiment Video

Updated: Mar 31, 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

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Development and Validation of Multinomial Logistic Regression and Machine Learning Algorithms for Identifying

Bingjie Wang1, Rajeev K Naidu2, Mark Hanly3

  • 1School of Optometry and Vision Science, University of New South Wales, Sydney, New South Wales, Australia.

Ophthalmic & Physiological Optics : the Journal of the British College of Ophthalmic Opticians (Optometrists)
|March 30, 2026
PubMed
Summary

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Models using ocular biometry and demographics can identify pre-myopic children. This approach may enhance population-based vision screening programs for early myopia detection.

Area of Science:

  • Ophthalmology
  • Biometry
  • Public Health

Background:

  • Pre-myopia detection is crucial for early intervention in school-aged children.
  • Existing vision screening methods may not effectively identify children at risk of myopia progression.

Purpose of the Study:

  • To develop and validate predictive models for identifying pre-myopic children.
  • Utilize ocular biometry and demographic data for enhanced myopia risk assessment.

Main Methods:

  • Applied multinomial logistic regression (MLR) and machine learning algorithms to data from 36,925 Chinese children (aged 6-15.99).
  • Key features included axial length (AL), AL/corneal radius of curvature ratio (AL/CR), age, and gender.
  • Developed age-specific and all-age models, with class weighting for pre-myopia detection.
Keywords:
Axial lengthMultinomial logistic regressionMyopia preventionOcular biometryPre-myopia

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Last Updated: Mar 31, 2026

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Main Results:

  • The optimal MLR model incorporated AL, AL/CR, age, gender, and a three-way interaction.
  • Achieved high Area Under the Curve (AUC) values: 0.874 (younger age) and 0.899 (all-age).
  • Pre-myopia detection sensitivity reached 81.5% with 71.9% specificity in the all-age model.

Conclusions:

  • Ocular biometry and demographic data effectively identify pre-myopia in children.
  • These models show promise for integration into population-based vision screening programs.
  • Supports early detection and intervention strategies for myopia.