Relevance. Diabetic retinopathy (DR) is a specific microangiopathic lesion of the retina and remains one of the leading causes of irreversible blindness among working‑age individuals worldwide. Early diagnosis of DR is often hindered by a shortage of specialized ophthalmologists and limited access to eye care, which highlights the importance of artificial intelligence (AI) technologies for automated screening. Purpose of the study. To evaluate the diagnostic performance of the AI system Retina AI in detecting diabetic retinopathy in primary healthcare settings of the Syrdarya region, Uzbekistan. Materials and methods. The study included 360 patients with type 1 and type 2 diabetes mellitus, with disease duration of at least 5 years. All participants underwent bilateral color fundus photography using the OPTOMED Aurora IQ retinal camera. The images were analyzed by primary care physicians and by the automated Retina AI system. Expert ophthalmologists served as the reference standard. Diagnostic performance was assessed by sensitivity, specificity, overall accuracy, and Cohen’s kappa (κ). Results. The Retina AI system demonstrated significantly higher sensitivity (92.3% vs. 74.5%; p<0.001) and overall accuracy (91.6% vs. 79.3%; p<0.001) compared to primary care physicians. The kappa coefficient was 0.89, indicating “very good” agreement with expert evaluations. The highest diagnostic accuracy was observed in detecting microaneurysms, hemorrhages, and hard exudates. Conclusion. Integration of Retina AI into primary healthcare practice ensures reliable detection of DR, reduces the risk of missed diagnoses, and optimizes screening programs in regions with limited ophthalmic resources.
| Mualliflar | A.U. Ismailov, A. F. Yusupov, М.Х. Каримова, Х.Ш. Хусанбаев |
|---|---|
| Jurnal | Передовая Офтальмология |
| Nashr sanasi | 2026-08-14 |
| Jild | 17 |
| Son | 2 |
| Betlar | 87-91 |
| DOI | 10.57231/j.ao.2026.17.2.018 |
DOI: 10.57231/j.ao.2026.17.2.018 · Maqolaning asl sahifasi · PDF
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