This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Clinical Information and Decision Making (1915) Artificial Intelligence (2129) Machine Learning (2076) Ophthalmology (67)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57644, first published .
Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis

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

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

Authors of this article:

Huiyi Zuo1 Author Orcid Image ;   Baoyu Huang1 Author Orcid Image ;   Jian He1 Author Orcid Image ;   Liying Fang1 Author Orcid Image ;   Minli Huang1 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. Chen X, Chen Y, Ding X, Wang Y, Shi D, He M. Optic neuropathy in high myopia. Visual Neuroscience 2025;42(1):0 View
  2. Zhang Z, Lv L, Chen D, Chen Y, Zhang W, Li F, Zhou J. Shaping the future of myopia with artificial intelligence: Mapping trends and promising directions. Advances in Ophthalmology Practice and Research 2025;5(4):245 View
  3. Alghamdi M, Almustanyir A, Alshuimi A, Alrasheed S, Alabdulkader B, Alanazi M, Altoaimi B, Bin Dulaym M, Alsamnan L, Alghamdi W. Prioritizing Pediatric Eye Care in Saudi Arabia: A National Delphi Consensus Study. Healthcare 2025;13(19):2467 View
  4. Yang Z, Wang M, Huang X, Luan R, Shao Y, Li X. Interpretable machine learning model based on blood parameters for screening high myopia. Eye 2025 View