AI-BASED EARLY DETECTION OF DIABETIC RETINOPATHY USING FUNDUS IMAGING

Abstract

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes worldwide. Early detection and timely intervention are critical to prevent irreversible retinal damage and preserve vision. Fundus imaging is a standard diagnostic tool for identifying retinal abnormalities, but manual interpretation is labor-intensive and prone to inter-observer variability. Artificial intelligence (AI) and deep learning techniques, particularly convolutional neural networks (CNNs), provide automated, accurate, and rapid analysis of fundus images, enabling early detection and classification of DR stages. This paper reviews current AI methodologies for diabetic retinopathy detection using fundus imaging, discusses challenges such as limited annotated datasets, image variability, and model interpretability, and highlights the potential of AI systems to enhance diagnostic accuracy, optimize screening programs, and improve patient outcomes.