# Artificial Intelligence-Based System for Retinal Disease Diagnosis
## 1 Introduction  
Preservation of human visual functions is a pressing issue for the medical community. Deterioration or loss of visual function has serious consequences not only for humans as individuals, but also as productive resources of the economy. The retina is the inner shell of the eye, consisting of many layers. The main ones are the pigment epithelium and light-sensitive cells responsible for converting light signals, which are then transmitted to the brain. As a result, a person receives a clear image of the environment in all its color diversity. The occurrence of pathological processes in various areas (periphery, central part) provokes deterioration in the acuity and quality of vision, as a result of which surrounding objects begin to be perceived as distorted, faded and unnatural. Untimely diagnosis and treatment of retinal pathologies are fraught with irreversible consequences, up to and including complete loss of vision. According to WHO estimates [1], by 2050 there will be more than 244 million people in the world aged over 65 years, which is 14.9% of the world's population, with retinal diseases such as diabetic retinopathy and age-related macular degeneration. Global estimates of the number of people with eye diseases [1] that can lead to visual impairment show howcommon such diseases are. At least 2.2 billion people worldwide live with some form of visual impairment or blindness. This figure includes people with vision impairment due to presbyopia (1.8 billion people, including people with corrected and uncorrected presbyopia), moderate to severe distance vision impairment or blindness due to uncorrected refractive errors (123.7 million people, such as myopia or hyperopia), cataracts (65.2 million), age-related macular degeneration (10.4 million), glaucoma (6.9 million), corneal opacity (4.2 million), diabetic retinopathy (3 million), trachoma (2 million) and other conditions (37.1 million) (Figure 1). Detection of retinal pathologies at early stages using advanced non-invasive diagnostic methods can significantly reduce the duration of treatment, speed up the recovery process and help a person return to active work. Currently, the following methods are most commonly used to diagnose retinal pathologies:  
1. Ophthalmoscopy is a procedure for examining the fundus of the eye, to assess the retina condition, optic nerve head and blood vessels of the eye using special equipment. 2. Optical coherence tomography (OCT) is a diagnostic method that has high resolution and provides highly detailed images of the fundus. 3. Electrophysiological diagnostic methods are methods based on recording bioelectrical activity, allowing analysis of the retina functional state based on electrical signals generated by retinal cells. All of these methods are among the most accurate and practically significant research methods, but they can be time-consuming and error-prone. Given the high need of ophthalmologists for fast and accurate diagnostics to make decisions about the treatment and prevention of diseases, interest in research in the field of artificial intelligence methods in ophthalmology has increased. Artificial intelligence (AI) technologies--machine learning, analytics of big data and generative neural networks--are actively being implemented to diagnose pathologies such as diabetic retinopathy, diabetic macular edema, age-related macular degeneration and others [2; 3; 4; 5]. A promising direction in this area is a method for analyzing medical images obtained using OCT or OCT angiography [6; 7]. The artificial intelligence program must be trained on a large array of data, including images of the fundus [8; 9]. An example of research in the field of diagnostics of retinal pathologies using artificial intelligence methods is a program to evaluate the structure of the fundus and conduct dynamic observation of the studied structure based on archival material of fundus photographs [10]. Figure 1: Structure of eye diseases in the world that can cause visual impairment as of 2020 year (compiled according to [1]).