# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 1 Introduction  
Inherited retinal diseases (IRDs) are a group of diseases belonging to rare diseases, also falling into a group of heterogeneous conditions. These diseases, due to the progressive degeneration of the retina, lead to loss of vision. This loss can occur due to dysfunction or degeneration of the retinal photoreceptors or the pigmented epithelium [1]. IRDs are mostly caused by Mendelian mutations in 1 of at least 300 different genes. The clinical symptoms associated with these diseases vary, not only according to the different subtypes of IRDs, but also according to the different disease genes [2]. This type of disease has a prevalence of approximately 1 in 3000 individuals, out of a total of more than 2 million people worldwide [3, 4]. The IRDs have a profound impact, not only on patients, but also on society, as they are the most frequent hereditary forms of human visual impairment [5]. Artificial intelligence (AI) based on deep learning has shown great potential in various medical fields, especially for well-defined clinical tasks where imaging data, such as autofluorescence ocular fundus (FAF) and optical coherence tomography (OCT), contain most of the relevant information for the specific analysis [6]. In medical areas such as radiology, pathology, and dermatology, which rely heavily on diagnostic imaging, ophthalmology shares this similarity and dependence on diagnostic imaging, making it the most prominent application of AI in healthcare [7]. The advantages brought by AI are truly advantageous, as it allows us to manage the complexity of 21 st century ophthalmology. In this way, using efficient algorithms that, in addition to detecting pathologies, are also able, from large volumes of image data, to help reduce diagnostic and therapeutic errors, promoting personalized medicine. All of this is possible by being able to \"learn\" characteristics from these data. In addition, AI can recognize specific disease patterns and gain innovative scientific insights by correlating new characteristics [6]. The use of deep learning in IRDs could be a valuable support tool for healthcare professionals, helping not only to detect these pathologies, but also to classify the gene responsible for them. This could accelerate diagnosis and enable the application of the appropriate treatment for each specific gene. Despite the great advances that artificial intelligence has made in medicine and, consequently, in ophthalmology, there has been a difficulty described by numerous authors in IRDs. They were faced with the limitation of small datasets for training deep learning models while developing their models for classifying and detecting this type of pathology [8; 9]. So, despite the success of using AI in retinal diseases, it has not yet been possible to exploit its full potential in IRDs. This is due to the scarcity of data on these diseases for use in training deep learning models, which have small datasets. This limitation has implications for the effectiveness and reliability of classifiers in these diseases, as identified by some authors [8; 9]. It is seen as a limitation because deep learning models need large amounts of data to train models to be more effective and have a greater capacity for generalization. In addition to the scarcity of data for training, the process of efficiently labeling data in the medical field is an expensive process, as it requires years of specialized training [10]. Some ways of overcoming this limitation are using traditional data augmentation techniques. These techniques are already used to overcome this difficulty and to balance and augment the training dataset. They use resampling approaches and random image transformations such as rotation and blurring. However, these techniques have been shown to have a moderate impact on model generalization [11].