Advanced Deep Learning Techniques for Automated Intra Retinal Layer Segmentation: Enhancing Precision and Efficiency with U-Net Architectures

The human eye is a highly evolved sensory organ that gives us the ability to see and comprehend our environment. AMD, or age-related macular degeneration, is a serious retinal condition. Globally, AMD affected about 196 million people in 2020; by 2040, that figure is predicted to rise to 288 million. AMD primarily affects older people. As people age, the prevalence rises; approximately 11% of those 65 to 74 years old and approximately 27% of those 75 years and older are affected. About one-third of people with diabetes have diabetic macular edema (DME), a common consequence of diabetic retinopathy (DR) which is one more type of retinal disorder. DME affects over 21 million individuals globally. The early detection and monitoring of retinal illnesses such as AMD and DME have been considerably aided by advancements in imaging and diagnostic technologies. OCT reveals irregularities like fluid accumulation or aberrant blood vessels, which aids in the diagnosis of retinal disorders. Prior to beginning anti-VEGF medication, this imaging is used as a baseline to assess the disease's severity. Retinal problems can be better diagnosed and treated if real-time OCT analysis is combined with AI-driven tools and standardized processes. This will assist to alleviate some of these concerns. When it comes to segmentation and feature extraction, OCT (Optical Coherence Tomography) image analysis is much enhanced by the use of U-Net architecture. The topology of U-Net is ideal for accurately segmenting the retina's many layers. The training of U-Net to identify particular pathological characteristics in OCT images enables automatic and precise detection and tracking of retinal illnesses.