Early diagnosis of diabetic retinopathy: current opportunities and challenges

Diabetic retinopathy is a microvascular complication of diabetes mellitus and remains one of the leading causes of irreversible vision loss among the working-age population. Aim: to summarize current approaches to the early diagnosis of diabetic retinopathy. Object and methods. An analytical review of scientific sources focusing on the clinical and instrumental diagnosis of diabetic retinopathy. Results. Fundus photo, optical coherence tomography and fluorescein angiography enable the detection of microvascular abnormalities and assessment of disease stage. However, shortages of ophthalmologists, limited availability of equipment, and the high cost of procedures hinder the widespread implementation of these methods and leave a proportion of patients without regular screening. Artificial intelligence algorithms demonstrate high sensitivity and specificity in detecting early manifestations and stages of the disease, reduce interobserver variability, and facilitate optimization of patient triage. Conclusion. Integration of artificial intelligence systems into diabetic retinopathy screening programs represents a promising approach to improving access to early diagnosis and preventing irreversible vision loss, provided that these technologies are used as an adjunct to, rather than a replacement for, clinical assessment by an ophthalmologist.

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