Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

Journal: Translational vision science & technology
PMID: 39120886

Abstract

PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

Authors

  • Bole Ying
    Lower Merion High School, Ardmore, PA, USA.
  • Rajat S Chandra
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Jianyong Wang
    1 Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China.
  • Hongguang Cui
    Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
  • Julius T Oatts
    Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA.

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