# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 4 Discussion
[42] used StyleGAN2 architecture, which made it possible to generate realistic images that were very similar to real images. In Epiretinal Membrane disease, [PERSON] et al. [43], using a StyleGAN2 architecture, was able to generate more realistic images with better quality than other methods used. Using these generated data to train the model also improved its performance in detecting this disease. Additionally, [PERSON] et al. [44], using joint WGAN and CGAN architecture, managed to generate high-quality images with great diversity to identify signs of hard exudate, which are retinal signs of disease. In Retinal Blood Vessel disease, [PERSON] et al. [45] used LDM to generate images with high quality and diversity in order to add to the training dataset and thus create a more comprehensive and diverse training set. On the other hand, using DCGAN architecture, [PERSON] and [PERSON] [46] were able to generate images that maintain the statistical distribution of the original data set, obtaining better results than other methods (Tub-GAN, DGAN, MI-GAN). In the retinopathy group, where they do not specify any specific type of disease, [PERSON] et al. [47], using a CycleGAN architecture, was able to generate images with realistic features and better quality. The accuracy of the classifiers was improved by using these data to train them. In the Retinopathy of Prematurity Disease, [PERSON] et al. [48], using Pix2 Pix HD architecture, generated realistic images that made it difficult for experts to distinguish between real and synthetic images. Using an ROP-GAN, [PERSON] et al. [16] managed to generate images of classes with little data and at various stages of the disease. Using these synthetic data to train the classifiers improved their accuracy. In studies that do not specify a retinal disease, but only refer to the group of retinal diseases, in segmentation problems, [PERSON] et al. [49] used DCGAN architecture to generate high-quality images. These synthetic data improved segmentation performance. Using StyleGAN2-ADA architecture, [PERSON] et al. [50] generated images to balance the dataset. Accuracy improved when the models were trained with synthetic data and the dataset balanced with these data. Using a DCGAN, [PERSON] et al. [51] generated images with reasonable detail. Using these images for training improved the model's performance by adding diversity and increasing its generalization capacity. Using CycleGAN, [PERSON] et al. [52] generated low-quality images and then synthesized them to generate high-quality, high-resolution images from these low-quality images. Additionally, [PERSON] et al. [53] generated OCT images that are very similar to real images. According to the metrics obtained, these images are comparable to real images. Most of the studies reviewed focus on the generation of synthetic FAF or OCT images based on the type of ophthalmic examination data used for training. Although the datasets (whether public or private) often come from several hospitals, few studies explicitly discuss the impact of domain shift, or the variability introduced by different imaging devices. This aspect was not widely explored in the studies reviewed, leaving open questions about how generative models deal with variations between datasets from different institutions and imaging modalities. It was found that the analyzed studies used several metrics to evaluate the results obtained, the most frequent being FID [54], ROC/AUC, ACC, SSIM [55], sensitivity, and specificity, as well as qualitative evaluations conducted by retinal specialists. The combination of multiple metrics in the evaluation of results reflects a more robust and reliable approach. Qualitative assessment by retinal specialists is the most noteworthy because, in many cases, it is difficult to distinguish real images from synthetic images. This is a metric that gives more confidence because it is carried out by specialists in the field, but it can lead to greater error because it depends heavily on the individual knowledge and experience of each specialist.