A Novel AI based Early Diabetic Retinopathy Detection using Retinal Images

Diabetic Retinopathy (DR), a common complication of diabetes, is a leading cause of blindness. Early detection is important to prevent vision loss. This study explores the application of artificial intelligence (AI) for early diagnosis of DR from retinal images. We highlighted the importance of early detection, the limitations of traditional methods, and present a comparative analysis of different AI techniques including Convolutional Neural Networks (CNN), transfer learning, ensemble methods, explainable AI, and an integrated, AI-interpretable approach that integrates with clinical workflows. The results indicate that these AI methods have different levels of sensitivity, specificity, and usefulness. The challenges and future directions of AI in the field of DR are also briefly explained. The application of AI in DR detection has the potential to revolutionize screening processes, improve access and accuracy, thereby improving patient outcomes and minimizing the global impact of DR on with visual health.