Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.

Journal: Translational vision science & technology
PMID: 37342054

Abstract

PURPOSE: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning.

Authors

  • Dan Huang
    Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.; Department of Anesthesiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Yingxiao Qian
    Department of Ophthalmology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China.
  • Saiguang Ling
    EVision Technology, Beijing, China.
  • Zhou Dong
    School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
  • Xin Ke
    EVision Technology (Beijing) Co. LTD, Beijing, China.
  • Qi Yan
  • Haohai Tong
    The Second Affiliated Hospital, Zhejiang University School of Medicine, Eye Center, Hangzhou, Zhejiang, China.
  • Zijin Wang
    Department of Ophthalmology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China.
  • Tengfei Long
    Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing, China.
  • Hu Liu
    School of Instrument Science and Opto-electronic Engineering, Beihang University, Beijing, 10091, China.
  • Hui Zhu

Keywords

External Resources

Popular Topics
Recent Journals