A fundus image dataset for intelligent retinopathy of prematurity system.

Journal: Scientific data
PMID: 38802420

Abstract

Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.

Authors

  • Xinyu Zhao
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • Shaobin Chen
    National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
  • Sifan Zhang
    Department of Neurobiology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China.
  • Yaling Liu
    Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
  • Yarou Hu
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Duo Yuan
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Liqiong Xie
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Xiayuan Luo
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Mianying Zheng
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Ruyin Tian
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Yi Chen
    Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Tao Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
  • Zhen Yu
  • Yue Sun
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Zhenquan Wu
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
  • Guoming Zhang
    Shenzhen Eye Hospital; Shenzhen Key Ophthalmic Laboratory, Health Science Center, Shenzhen University, The Second Affiliated Hospital of Jinan University, Shenzhen, China. Electronic address: 13823509060@163.com.

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