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Related Experiment Video

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Predicting myopia risk using a machine learning model based on fundus imageomics.

Xiaoling Zhang1,2, Zixun Wang2, Jingtao Yu2

  • 1Handan Eye Hospital (The Third Hospital of Handan), Handan, Hebei, China.

Scientific Reports
|December 12, 2025
PubMed
Summary

Machine learning models using retinal images can predict myopia risk in children. The best model identified key retinal features for early myopia risk stratification.

Keywords:
Axial length/Corneal curvature ratio (AL/CR)Color fundus photography (CFP)Deep learning imageomicsMachine learning (ML)Myopia risk prediction

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Area of Science:

  • Ophthalmology and Computational Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Myopia is a growing global health concern, particularly in school-aged children.
  • Early prediction of myopia risk is crucial for timely intervention and management.
  • Quantitative analysis of retinal features from color fundus photography (CFP) offers potential for risk assessment.

Purpose of the Study:

  • To develop a machine learning (ML) model using CFP data to predict myopia risk in children.
  • To identify key retinal imageomics features associated with myopia progression.
  • To evaluate the predictive performance of various ML algorithms for myopia risk stratification.

Main Methods:

  • A cross-sectional study involving 2,184 children aged 6-10 years with CFP data.
  • Extraction of 146 retinal imageomics features using the EVisionAI platform, alongside age and sex.
  • Feature selection via LASSO regression and expert review, followed by model construction using RF, XGBoost, and LightGBM.

Main Results:

  • The Random Forest (RF) model achieved the highest predictive performance (AUC = 0.798), outperforming LightGBM and XGBoost.
  • Key predictors identified include age, nasal disc-foveal distance, atrophic areas, and vascular parameters.
  • The RF model demonstrated high specificity (0.80) and moderate sensitivity (0.59), with strong calibration.

Conclusions:

  • Quantitative CFP-derived imageomics combined with ML can effectively predict myopia risk in school-aged children.
  • The developed RF model, utilizing age, retinal distances, and vascular features, shows significant clinical value.
  • This approach offers a promising tool for early myopia risk stratification and personalized management strategies.