Aspects Developed for the Detection of Diabetic Retinopathy: A Systematic Literature Review

Cárdenas J.; Santisteban J.; Morales V.; Morales M. · Lecture Notes in Networks and Systems (2026) · DOI: 10.1007/978-3-031-98768-7_43

Resumen

Diabetic retinopathy (DR) can lead to blindness without timely detection and treatment. Traditional diagnosis, through ophthalmologic examinations, is laborious, expensive, and limited in remote areas. Early detection of DR presents challenges as it involves manual review of retinal images, making it a slow and costly process. Machine learning (ML) emerges as a promising alternative. As a branch of artificial intelligence, it allows computers to learn tasks without explicit programming. In detecting DR, ML can train models to identify signs of the disease in retinal images. This article reviews advances in ML models to detect DR, demonstrating efficacy in early detection. Models based on convolutional neural networks (CNN), achieve accuracy comparable to that of physicians or optometrists with greater precision by overcoming human subjectivity, speed in image analysis, and utility in remote environments. Although further studies are needed to confirm results and address challenges, ML emerges as a valuable tool in the fight against blindness caused by DR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Peruana de Ciencias Aplicadas