Exploring the Intersection of AI and Diabetes: Insights through Non-invasive Technique Using Retina

Diabetes mellitus, a major threat to global health, can have serious complications, like diabetic retinopathy, the primary cause of blindness in people of working age. Novel developments in artificial intelligence (AI) present encouraging prospects for the timely identification, diagnosis, and treatment of diabetic retinopathy via retinal imaging. The focus of this research is on how deep learning models and machine learning algorithms are revolutionizing retinal screening procedures as it examines the relationship between AI and diabetes management. Artificial intelligence (AI) algorithms are able to recognize small changes in retinal structures with great accuracy, frequently outperforming human specialists, by evaluating large databases of retinal images. This paper explores the application of a customized VGG model, which is fine-tuned to classify diabetic retinopathy images into five severity levels with an accuracy of 93.75%. The customised VGG model is benchmarked against existing AI frameworks, including VGG 16 and VGG 19 to validate its performance. Due to the ability to intervene promptly, these advancements not only improve treatment outcomes but also aid in early detection. Moreover, the examination of AI integration into clinical practice looks at issues including data protection, ethical constraints, and the requirement for ongoing learning to keep up with changing medical knowledge. The research highlights how AI has the potential to transform diabetes treatment and lessen the prevalence of visual loss worldwide in its conclusion.