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

Formative Evaluation of Digital Health Interventions (3229) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (717) Mobile Health (mhealth) (3464) mHealth for Screening (296) Ophthalmology (67) Health Services in Resource-Poor Settings and LMICs (542) mHealth for Wellness, Behavior Change and Prevention (3315)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55270, first published .
Using Smartphones to Enhance Vision Screening in Rural Areas: Pilot Study

Using Smartphones to Enhance Vision Screening in Rural Areas: Pilot Study

Using Smartphones to Enhance Vision Screening in Rural Areas: Pilot Study

Authors of this article:

Zheng Wang1 Author Orcid Image ;   John Kempen2, 3, 4, 5 Author Orcid Image ;   Gang Luo2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Anitha V, Ravindran M. Artificial intelligence in pediatric eye care: Assessing the necessity and impact. Indian Journal of Ophthalmology 2025;73(8):1219 View
  2. Syauqie M, Patria H, Hastono S, Siregar K, Moeloek N. Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image. Frontiers in Computer Science 2025;7 View
  3. Varshney A, Parmar G. Evaluating the Diagnostic Accuracy of a Portable Autorefractor for Refractive Errors and Strabismus Detection in Children: A Multiclinic and Cross-sectional Study. Delhi Journal of Ophthalmology 2025;35(3):230 View
  4. Yan H, Yi Z, Qilin S, Hong C, Yan H, Wei T. Deep learning–empowered low‐cost portable automated refraction system: A solution to the inadequate effective correction rate of refractive errors in resource‐limited areas. Ophthalmic and Physiological Optics 2025 View