Detecting Eye Diseases Via Hybrid Deep Learning Algorithms From Fundus Images

Eye health is of great importance for quality of life. Some eye diseases can progress and cause permanent damage up to vision loss if they are not treated early. Therefore, it is of great importance to have regular eye examinations and to detect possible eye diseases before they progress. Healthy people should also undergo eye screening once a year, and those with any complaints regarding eye health should be examined. With the advancing technology, Artificial Intelligence (AI) has begun to play a significant role in the healthcare sector. Retinal diseases, serious health problems resulting from damage to the back part of the eye's retina, include conditions such as retinopathy, macular degeneration, and glaucoma. Artificial intelligence, with its visual recognition and analysis capabilities, holds great potential in the early diagnosis of retinal diseases. AI-based diagnosis of retinal diseases typically involves the use of specialized algorithms that analyze retinal images. These algorithms identify abnormal features in the eye, providing doctors with a quick and accurate diagnosis. EyeCheckup v2.0 will diagnose glaucoma suspicion, severe glaucoma suspicion, age-related macular degeneration diagnosis, RVO diagnosis, diabetic retinopathy diagnosis and stage, presence/absence of DME suspicion and other retinal diseases from fundus images. This study is designed to assess the safety and efficacy of EyeCheckup v2.0. The study is a single center study to determine the sensitivity and specificity of EyeCheckup to retinal and optic disc diseases. EyeCheckup v2.0 is an automated software device that is designed to analyze ocular fundus digital color photographs taken in frontline primary care settings in order to quickly screen.