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Open Angle Glaucoma: Treatment01:27

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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.

Yudistira Yudistira1, Kevin Anggakusuma Hendrawan2, Ari Andayani3

  • 1Widya Mandala Catholic University Faculty of Medicine, Surabaya, Indonesia.

Turkish Journal of Ophthalmology
|October 27, 2025
PubMed
Summary

Artificial intelligence (AI) tools show high accuracy in detecting diabetic retinopathy (DR) for screening in developing countries. Portable fundus cameras and trained non-specialists also offer effective DR detection, improving accessibility to eye care.

Keywords:
Diabetic retinopathy screeningartificial intelligencedeveloping countriesnon-specialistportable fundus camera

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Area of Science:

  • Ophthalmology
  • Medical Technology
  • Public Health

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss, with increasing global prevalence.
  • Effective screening is crucial, especially in developing nations with limited healthcare resources.
  • Current screening methods face challenges in accessibility and specialized personnel.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of different diabetic retinopathy (DR) screening methods.
  • To assess artificial intelligence (AI)-based tools, portable fundus cameras, and trained non-ophthalmologists in a developing country context.

Main Methods:

  • A systematic literature search was performed across major databases (ScienceDirect, PubMed, Cochrane Library).
  • Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.
  • A meta-analysis of 21 AI-based studies was conducted to determine pooled sensitivity and specificity for DR detection.

Main Results:

  • The meta-analysis included 25 studies, with 21 focusing on AI tools.
  • Pooled sensitivity and specificity for AI in detecting any DR were 0.890 and 0.900, respectively.
  • AI demonstrated high accuracy for referable DR (0.933 sensitivity, 0.903 specificity) and vision-threatening DR (0.891 sensitivity, 0.936 specificity).
  • Portable fundus cameras and general physicians showed good agreement with gold standards.

Conclusions:

  • AI-assisted DR screening shows significant potential in resource-limited settings.
  • Portable fundus cameras and task-shifting to trained non-specialists complement AI, enhancing DR screening accessibility.