AI in Retinal Image Analysis for Diabetic Retinopathy Detection

Abstract

Diabetic Retinopathy (DR) is a leading cause of blindness among individuals with diabetes, necessitating early detection and intervention. Traditional diagnostic methods, though effective, are time-consuming and reliant on skilled professionals, creating a barrier to timely treatment. Artificial Intelligence (AI), particularly deep learning algorithms such as Convolutional Neural Networks (CNNs), has emerged as a promising solution for automating the detection of DR in retinal images. AI models can accurately identify key features of DR, such as microaneurysms, hemorrhages, and exudates, enabling early diagnosis even in resource-limited settings. This paper explores the integration of AI into retinal image analysis for DR detection, discussing its mechanisms, advantages, challenges, and impact on clinical practice. With its potential for improving diagnostic accuracy, reducing costs, and increasing accessibility, AI is poised to revolutionize the management of DR, providing faster and more reliable screening to prevent vision loss in diabetic patients globally.