Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm

Mingjun Gao ¹, Yanhua Hou ¹, Yutong Lu ¹

⁰Department of Ophthalmology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.

Frontiers in Medicine +

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December 3, 2025

View abstract on PubMed

Summary

This study found that premyopic children experience rapid myopia progression. Machine learning models accurately predict myopia onset and shift, aiding in early intervention for at-risk children.

Area Of Science

Ophthalmology
Pediatric Ophthalmology
Computational Biology

Background

Myopia is a growing global health concern, particularly in school-aged children.
Early identification of children at risk for myopia progression is crucial for timely intervention.
Understanding longitudinal changes in ocular parameters is key to predicting myopia development.

Purpose Of The Study

To investigate longitudinal changes in ocular parameters in premyopic children.
To develop and validate a machine learning model for predicting myopia onset and shift within one year.
To identify key predictive factors for myopia progression in this cohort.

Main Methods

Prospective cohort study of 320 premyopic children (aged 6-12 years).
Regular measurements of visual acuity, spherical equivalent (SE), axial length (AL), corneal curvature (CC), and subfoveal choroidal thickness (SFCT).
Machine learning algorithms employed for prediction, with SHAP analysis for feature interpretation.

Main Results

49.3% of participants developed myopia within one year.
Significant annual SE progression (-0.695 D) and AL elongation (0.356 mm).
Machine learning model achieved high accuracy (AUC-ROC 0.963) for myopia onset prediction, with SE, parental myopia, SFCT, and age as key predictors.

Conclusions

Premyopic children demonstrate accelerated myopia progression.
Machine learning models offer a promising approach for predicting myopia onset and progression.
These predictive models can facilitate risk stratification and targeted prevention strategies.

Keywords:

machine learning myopic progression prediction model premyopia subfoveal choroidal thickness