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August 17, 2025Scientific ReportsOpen Access

Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only

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Authors

YRYonina RonTRTchelet RonNFNaomi Fridman

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Overview

Analysis predicts myopia risk in children from routine pediatric eye examinations, suggesting advanced screening methods.

Key Points

  • Models predicted myopia with sensitivity reaching up to 77% and specificity at 92%, indicating strong accuracy.
  • Data were sourced from 7814 visits among 2437 pediatric patients, ensuring a comprehensive analysis of myopia development.
  • Machine learning algorithms, specifically random forest and gradient boosting tree, were utilized for creating predictive models.
  • This approach provides caregivers with an advanced tool for identifying children at risk of myopic refractive error, enhancing personalized care.

Cite This Study

Ron et al. (2025) studied this question.

synapsesocial.com/papers/68af4754ad7bf08b1ead3d22https://doi.org/10.1038/s41598-025-13990-5
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