## 2 _Introduction_  
Artificial neural networks (ANN) are an attempt to simulate the structure and functions of the brain specific to living organisms. A key feature of these is the fact that, starting from a lot of examples, a certain model of the problem can be synthesized. The neural network practically builds its own algorithm in solving a problem, if it is trained by providing multiple particular cases, from which it extracts the necessary information. Based on the collected data, the network is trained, i.e., the process behavior is memorized. During the work or reference stage, the network will use the acquired information to process similar situations like those involved in the training set. This represents the ability to generalize, based on which the ANN can operate correctly with data that were not presented during the training process [1]. The most commonly used networks are those with forward propagation. They are characterized by the presence of a layer of input neurons, a number of hidden layers, and a layer of output neurons. Networks have the ability to learn from examples, which makes them highly flexible and stable in medical diagnosis. They have the ability to generalize and operate with data different from those presented in the training stage. The back propagation algorithm is also called the generalized delta algorithm because it extends the method of the adaline network (delta rule), and it is based on the difference between the desired and the real output, driven by the descending gradient method (the gradient tells us how a function varies in different directions). ANNs have been widely used not only in industry but also in scientific research to perform various tasks: data processing, shape recognition, and medical diagnosis. Neural networks were defined and introduced for the first time between 1950 and 1951 [2]. They have been widely used in various fields such as cardiology, oncology, and ophthalmology. Due to their predictive role, they have been used to diagnose sick patients in clinical practice [3; 4; 5; 6]. In ophthalmology, the detection of visual function abnormalities plays a key role in diagnosing eye diseases. Most neural networks have been used to measure retinal structures and visual function. [PERSON] et al. [7; 8; 9; 10; 11] used various artificial intelligence tools (neural networks and genetic algorithms) to determine a possible progression of the visual field in glaucoma patients, to classify changes in diabetic retinopathy (presence or absence of signs of retinopathy) and glaucoma stage [1; 11; 12]. The most common use of neural networks in ophthalmology has been in early diagnosis of glaucoma, when a doubtful diagnosis occurred. Neural networks have played an essential role in determining the need for early antiglaucoma therapy to prevent disease progression [13]. Applications that involve a neural network driven by data from medical records are intended to help physicians, not to replace them. A neural network generates a rule based on a large volume of input data. They provide the information needed for patient care and increase the amount of information available to a doctor. Developing a system that uses a neural network to help a physician is difficult because valid training data is needed, which the network can use to generate rules [1]. In its simplest version, the structure of a neural network involves three layers. The network receives the input vector and propagates the signal forward, layer by layer, until the output is generated. The error signal is propagated back from the output layer to the input layer, adjusting the network weights. With a possible infinite number of neurons, the network can approximate any real constant function [1]. However, the use of an additional layer can greatly decrease the number of neurons needed in the hidden layers and, obviously, can increase the modeling performance.