AUTOMATED DETECTION OF DIABETIC RETINOPATHY USING AI

Abstract

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among diabetic patients worldwide. Early detection and timely intervention are crucial for preventing disease progression. Manual screening of retinal fundus images is labor-intensive, requires expert ophthalmologists, and is prone to inter-observer variability. Artificial intelligence (AI), particularly deep learning algorithms, offers automated, accurate, and efficient solutions for DR detection. This paper reviews current AI-based methodologies for automated DR diagnosis, focusing on convolutional neural networks (CNNs), image segmentation, and hybrid approaches. Challenges such as dataset variability, annotation limitations, and model interpretability are discussed. The study highlights the potential of AI-driven systems to enhance diagnostic accuracy, optimize clinical workflow, and improve patient outcomes in diabetic retinopathy management.