Image Analysis-Based Machine Learning for the Diagnosis of Retinopathy of Prematurity: A Meta-analysis and Systematic Review.

Journal: Ophthalmology. Retina
PMID: 38237772

Abstract

TOPIC: To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications.

Authors

  • Yihang Chu
    Central South University of Forestry and Technology, Changsha, Hunan, China; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
  • Shipeng Hu
    Central South University of Forestry and Technology, Changsha, Hunan, China.
  • Zilan Li
    Department of Biochemistry, McGill University, Montreal, Quebec, Canada.
  • Xiao Yang
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Xianglong Yi
    Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, China. Electronic address: xly1010@sina.com.
  • Xinwei Qi
    State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China. Electronic address: xwqi1982@gmail.com.

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