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September 10, 2025Journal of Translational MedicineOpen Access

School-level prediction and management of myopia in children and adolescents

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Authors

SXShengsong XuSouthern Medical University Shenzhen HospitalLLLinling LiShenzhen UniversityYZYingting ZhuSun Yat-sen University

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Overview

AI models predict myopia occurrence and progression in non-myopic children, highlighting school-level application.

Key Points

  • The occurrence of myopia was 45.5% after two years among 915,991 children and adolescents.
  • Machine learning models achieved an AUC of 0.962 for predicting myopia onset and 0.923 for progression.
  • Data were analyzed from 870 schools across seven cities in China over a two-year period.
  • User-friendly software developed enhances accessibility of models for school-based myopia management.

Cite This Study

Xu et al. (2025) studied this question.

synapsesocial.com/papers/68c1b81854b1d3bfb60ec49dhttps://doi.org/10.1186/s12967-025-06855-y
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm2025
  2. 2Development of artificially intelligent tool for analysis and prediction of myopia progression among school-going children2026
  3. 3Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only2025
  4. 4Optimizing myopia prediction in children and adolescents using machine learning: a multi-factorial risk assessment model2025
  5. 5Revolutionizing Pediatric Myopia Care: A Machine Learning Approach for Rapid and Accurate Pre-clinical Screening2026