Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39507, first published .
Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Journals

  1. Li W, Tu Y, Zhou L, Ma R, Li Y, Hu D, Zhang C, Lu Y. Study of myopia progression and risk factors in Hubei children aged 7–10 years using machine learning: a longitudinal cohort. BMC Ophthalmology 2024;24(1) View
  2. Xu S, Li L, Han W, Zhu Y, Hu Y, Li Z, Ruan Z, Zhou Z, Zhuo Y, Fu M, Yang X. Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset. Ophthalmology and Therapy 2024;13(7):2009 View
  3. Ng Yin Ling C, Zhu X, Ang M. Artificial intelligence in myopia in children: current trends and future directions. Current Opinion in Ophthalmology 2024;35(6):463 View
  4. Hopf S, Schuster A. Epidemiologie der Myopie: Prävalenz, Risikofaktoren und Auswirkungen der Myopie. Klinische Monatsblätter für Augenheilkunde 2024;241(10):1119 View
  5. Fulton J, Leung T, McCullough S, Saunders K, Logan N, Lam C, Doyle L. Cross‐population validation of the PreMO risk indicator for predicting myopia onset in children. Ophthalmic and Physiological Optics 2024 View