EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY.

Journal: Retina (Philadelphia, Pa.)
PMID:

Abstract

PURPOSE: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).

Authors

  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Kaide Huang
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China; and.
  • Rong Ju
    Department of Neonatology, Chengdu Women & Children's Central Hospital, Chengdu, PR China.
  • Yuanyuan Chen
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Mengyu Li
    Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Shijun Yang
    Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Wentao Dong
    School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.
  • Miao Liu
    The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China.
  • Sizhu Chen
    Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Hongmei Leng
    Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Jie Zhong
    Department of Ophthalmology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, PR China. Electronic address: zjllxx1968@163.com.