Emerging Technologies in Diabetic Retinopathy Screening: Artificial Intelligence, Teleophthalmology, and Automated Approaches for Early Detection
DOI:
https://doi.org/10.64784/230Palabras clave:
Diabetic retinopathy, artificial intelligence, deep learning, automated diagnosis, retinal imaging, teleophthalmology, diabetic screening, machine learning, ophthalmology, digital health, early detection, fundus photography, autonomous systems, preventive medicine, healthcare technologyResumen
Diabetic retinopathy is one of the most common microvascular complications of diabetes mellitus and remains a leading cause of preventable blindness worldwide. The increasing prevalence of diabetes has intensified the need for effective screening strategies capable of identifying retinal abnormalities before irreversible visual impairment occurs. Conventional screening programs have demonstrated clinical effectiveness; however, limitations related to specialist availability, healthcare accessibility, geographic barriers, and increasing patient demand continue to restrict their overall impact. This review aimed to analyze current scientific evidence regarding emerging technologies for the automated early diagnosis of diabetic retinopathy, with particular emphasis on artificial intelligence, deep learning systems, teleophthalmology, automated retinal image analysis, and portable imaging platforms. A structured review of contemporary literature was conducted using peer-reviewed scientific articles, international clinical guidelines, validation studies, and technological reports obtained from major biomedical databases. The findings revealed that deep learning algorithms and autonomous artificial intelligence systems can achieve diagnostic performance comparable to experienced ophthalmologists while improving screening efficiency and scalability. Teleophthalmology networks and smartphone-based retinal imaging technologies demonstrated additional value by expanding access to retinal evaluation in underserved and geographically isolated populations. Despite promising outcomes, challenges related to algorithm transparency, regulatory oversight, data privacy, implementation costs, and equitable healthcare integration remain significant considerations. Overall, the available evidence supports the incorporation of automated diagnostic technologies as complementary tools within diabetic retinopathy screening programs. Continued technological development and clinical validation may contribute substantially to reducing diabetes-related visual impairment and improving preventive ophthalmologic care worldwide.
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