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Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Jingyun Wang1, Reed M Jost2, Eileen E Birch2,3

  • 1State University of New York College of Optometry, New York, NY, USA.

Translational Vision Science & Technology
|May 29, 2024
PubMed
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Optical biometric components in children with hyperopia change with age, with axial length growth offset by reduced corneal power. A machine learning model accurately predicts axial length using refractive error, age, and sex.

Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Pediatric Optometry

Background:

  • Hyperopia is common in children and can impact visual development.
  • Understanding the changes in ocular biometric parameters with age is crucial for managing pediatric hyperopia.
  • Existing methods for axial length estimation may not be optimal for this population.

Purpose of the Study:

  • To investigate the development of optical biometric components in children with hyperopia.
  • To apply a machine-learning model for predicting axial length in this cohort.
  • To provide updated reference data for ocular biometry in pediatric hyperopia.

Main Methods:

  • Recruited children aged 3-11 years with hyperopia (+1 D to +10 D).
  • Measured axial length, anterior chamber depth, lens thickness, central corneal thickness, and corneal power.

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  • Utilized classification and regression tree (CART) analysis to predict axial length using spherical equivalent (SEQ), age, and sex.

    • Main Results:

    • Ocular biometric parameters, including axial length and corneal power, showed significant age-related differences.
    • Axial length growth was observed to be offset by reductions in corneal power with increasing age.
    • The CART model predicted axial length with a lower mean absolute error (0.60 D) compared to linear regression (0.76 D).

    Conclusions:

    • Ocular biometric parameters evolve with age in hyperopic children, despite similar refractive errors.
    • Axial length increases are compensated by decreases in corneal power.
    • A machine-learning approach offers a more accurate method for estimating axial length in pediatric hyperopia.