AI-Driven Approach for Measuring and Classifying Diabetic Retinopathy Severity

Diabetic Retinopathy is one of the most common complications affecting people with diabetes and causes blindness in the world. Advanced technological methods through image analysis and artificial neural networks have become big assets in handling the escalating problem of DR. This paper discusses some of the approaches of implementing automation in DR detection with relation to the image acquisition and preprocessing, feature extraction and the actual classification employing the aid of AI. We review the utility of these systems in relation to costs, benefits, and performance, as well as some of the problems tied to the data, model interpretability and regulatory requirements. It demonstrates that automation holds the key to delivering higher patient-impact opportunities in clinical applications such as screening programs and telemedicine. Last, we discuss directions for future research and utilization in the community with an emphasis on the value of highly controlled, naturalistic designs and the translation of the findings into clinical practice.