Machine learning-driven prediction of cycloplegic refractive error in Chinese children.

Journal: Frontiers in cell and developmental biology
Published Date:

Abstract

OBJECTIVE: To develop and validate machine learning (ML) models for predicting cycloplegic spherical equivalent refraction (SER) using non-cycloplegic parameters, addressing challenges in pediatric ophthalmic assessments.

Authors

  • Bichi Chen
    Vision X Medical Technology Co., Ltd., Shanghai, China.
  • Li Tian
    Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China. tianlixy3@csu.edu.cn.
  • Fuyue Tian
    National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Qiaochu Yang
    National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Ying Ruan
    Shanghai E-Vision Eye Clinic, Shanghai, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Min Cao
    Guangzhou Panyu Sanatorium, Guangzhou, Guangdong, China.
  • Chuanyan Wu
    School of Control Science and Engineering, Shandong University, Jingshi Road, Jinan, 250061, China.
  • Maoyuan Yang
    National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Suzhong Xu
    National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Ruzhi Deng
    National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.

Keywords

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