Abstract:  Artificial Intelligence (AI) is growing very fast in healthcare. One important area is ophthalmology. Retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration can cause blindness if not detected early. Many patients do not show symptoms in early stages. Because of this, early diagnosis becomes very important. Traditional diagnosis depends on expert doctors. Doctors manually examine retinal images such as fundus images and OCT scans. This process takes time and may lead to human error. In many areas, trained ophthalmologists are not available. This creates a need for automated systems. AI-based methods can help in solving this problem. These methods use machine learning and deep learning models. They can analyze and detect disease from retinal images automatically with high accuracy. Studies show that deep learning models can achieve more than 90% sensitivity in detecting retinal diseases [1]. AI models can also detect small patterns that are not visible to human eyes. Recent advancements in imaging technologies like Optical Coherence Tomography (OCT) provide high-resolution images of retinal layers. When combined with AI, these images can be used for early diagnosis [2]. AI is also being used in real-world applications such as mobile screening tools and telemedicine systems.

This paper studies AI-driven methods for automated retinal disease analysis. It discusses imaging techniques, deep learning models, and RFMID dataset. A detailed methodology is also proposed. The paper also highlights challenges and future research directions.