# Artificial Intelligence-Based System for Retinal Disease Diagnosis
## 1 Introduction
Diagnostics of diabetic retinopathy based on a Web service using machine learning methods is proposed in [11; 12; 13]. The neural network is also trained on fundus images. From a medical point of view, OCT may not be effective enough due to concomitant eye pathologies, including vitreous pathologies, which may interfere with the study using the OCT method. From the point of view of implementing the technology, certain difficulties also arise, which are pointed out by the authors of the study [14]: creating a database for training artificial intelligence, collecting the required amount of data for training AI, <<black box>> problems and difficulties in communication between technical specialists implementing artificial intelligence and medical staff. Decision support systems improves the quality of medical diagnostics and treatment. Algorithms and knowledge bases ensure data analysis, accurate diagnosis and development of effective treatment recommendations. They make it easier for doctors to make informed decisions based on clinical data and scientific research, which leads to increased treatment efficiency. The study [15] proposes the development of an automated decision support system for an ophthalmologist to solve problems of morphological description of the state of the optic nerve head in norm and pathology. Electrophysiological methods for diagnosing retinal pathologies, which are free from the disadvantages of the OCT method and can be used in conjunction with AI, remain no less informative. The work [16] presents a decision support algorithm for diagnosing retinal dystrophy based on the analysis of electroretinogram signals using machine learning methods. The described algorithm analyzes the parameters obtained from the wavelet scalogram of the electroretinogram signal and classifies them into <<healthy>> and <<sick>>. A prototype of an intelligent (expert) decision support system based on expert knowledge is created to help doctors analyze and diagnose retinal diseases early [17; 18]. For intelligent data analysis and diagnostics, the authors use convolutional deep learning artificial neural networks and cognitive graphics and fuzzy logic methods for figurative representation of retinal pathologies in various diseases and representation of expert knowledge. The electroretinograms and statistical data on patients (gender, age, etc.) as initial data are included. The complexity of the proposed expert system is in the development of a biophysical research results database and storing of patient clinical data and medical examination results. An advantage of this work is using additional information to research data, for example, measurements from other devices. A system for diagnosing retinal pathologies based on fuzzy logic methods is described in [19; 20; 21; 22], which identifies up to nine different diagnoses. The input data for this system were electroretinogram data and a dynamic model of the retina. This work demonstrates the success of using such systems to diagnose retinal pathologies, but does not take into account the patient's medical history and socio-demographic data. The fuzzy logic method allows the ambiguity and uncertainty in data and knowledge that may arise when solving medical problems to be taken into account. This is especially important in cases where symptoms or diagnosis are not clear and ambiguous and decisions must be made under uncertainty. The fuzzy logic method also allows medical experience and expert opinion to be included in making decisions. This is especially useful in medicine, where it is often necessary to combine data from different sources and consider many factors in diagnosis or prescribing treatment. The tuning of the parameters of the fuzzy model can be implemented based on various algorithms, based on the new algorithms moth-flame optimizer (MFO) [23; 24], generalized opposition moth flame optimization (GCMFO) [25] and chaos simulated annealing multi-universe optimization (CSAMVO) [26].