Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
PMID: 39753217

Abstract

BACKGROUND: In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.

Authors

  • Huiyi Zuo
    Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.
  • Baoyu Huang
    Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.
  • Jian He
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
  • Liying Fang
    Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.
  • Minli Huang
    Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.

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