Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis

Diabetic retinopathy (DR) is one of the primary causes of vision loss among people living with diabetes and is expected to rise globally in the coming years. Effective screening strategies are essential, particularly in developing countries where resources and access to specialized care are limited. Our objective was to assess how accurately different screening methods detect DR, specifically artificial intelligence (AI)-based tools, portable fundus cameras, and trained non-ophthalmologist personnel, implemented in a developing country.

A literature search was conducted in ScienceDirect, PubMed, and the Cochrane Library. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. While all included studies were reviewed qualitatively, only those evaluating AI-based screening tools were included in the meta-analysis. Meta-analysis was performed using MetaDisc 2.0 to calculate pooled sensitivity, specificity, diagnostic odds ratio, and likelihood ratios for any DR, referable DR, and vision-threatening DR.

A total of 25 studies were included, with 21 AI-based studies eligible for the meta-analysis. The pooled sensitivity and specificity respectively were 0.890 (95% confidence interval [CI]: 0.845-0.924) and 0.900 (95% CI: 0.832-0.942) for any DR, 0.933 (95% CI: 0.890-0.960) and 0.903 (95% CI: 0.871-0.928) for referable DR, and 0.891 (95% CI: 0.393-0.990) and 0.936 (95% CI: 0.837-0.977) for vision-threatening DR. Meta-regression identified camera type as a significant factor. Portable fundus cameras and general physicians showed good agreement with the gold standards.

These findings support the potential of AI-assisted DR screening in low-resource settings and highlight the complementary roles of portable imaging and task-shifting to trained non-specialists.