# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 4 Discussion  
This study found some important points about the work being carried out in the field of Artificial Intelligence in the context of inherited retinal diseases, in particular, data augmentation using generative methods. A search of the literature revealed that although there is a great shortage of data in the IRDs as a group [8; 9], there are not many studies dealing with these techniques. As of the date of this review, only two studies have been found on IRDs that used generative methods for data augmentation. Regarding these two studies, in [PERSON] et al. [26], they were able to generate new images of unbalanced classes to augment the data from a few real images. This resulted in better classifier performance by using the synthetic images alongside the real ones in the training set and model training. The same happened in a study conducted by [PERSON] et al. [14], which also managed to generate images with a high level of realism that misled clinical experts in the field of ophthalmology, who wrongly assessed some of them as real and synthetic. Using these data for training did not worsen the classifier's performance, keeping it like using real images for training. In Age-related Macular Degeneration, the authors, using StyleGAN2-ADA [PERSON] et al. [27], a StyleGAN2 [PERSON] et al. [28], a StyleGAN [PERSON] et al. [29], and a ProGAN P. M. [PERSON] et al. [30] were able to generate high-resolution synthetic images, like real ones, from just a few images. These new data made it difficult for ophthalmologists to distinguish between real and synthetic data. In [PERSON] et al. [31], which used architecture composed of DCGAN and WGAN-GP, high-quality images were generated and, like [PERSON] et al. [27], using the generated data to train the classifiers improved their accuracy. In the case of Geographic Atrophy, which is a subtype of Age-related Macular Degeneration, [PERSON] et al. [32] used a VR-GAN architecture that generated high-quality images, some of which were better than real images. In Crystalline Retinopathy disease, [PERSON] et al. [33], using CycleGAN architecture, managed to generate realistic images of the pathology. Using these data in training as data augmentation improved the model's accuracy. By using the RV-GAN architecture for degenerative retinal diseases, [PERSON] et al. [34] were able to generate new, higher quality data for monitoring and tracking this type of disease. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study Context** & **Frequency** & **Percentage (\%)** \\ \hline Diabetic Retinopathy & 9 & 28.13 \\ Age-Related Macular Degeneration & 6 & 18.75 \\ Retinal Diseases & 5 & 15.63 \\ Inherited Retinal Disease & 2 & 6.25 \\ Retinopathy & 2 & 6.25 \\ Retinopathy of Prematurity & 2 & 6.25 \\ Retinal Blood Vessel & 2 & 6.25 \\ Crystalline Retinopathy & 1 & 3.13 \\ Degenerative Retinal Diseases & 1 & 3.13 \\ Epithelial Membrane & 1 & 3.13 \\ Hard Exudate & 1 & 3.13 \\ \hline Total & 32 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Context identified in the studies. Diabetic Retinopathy is where we have more studies with different architectures. [PERSON] et al. [35], which used a StyleGAN, [PERSON] and [PERSON] [36], which used a WGAN, and in [PERSON] et al. [37], which used a DCGAN, were able to generate realistic images, with variety for data augmentation and balancing classes in the datasets. Using DiaGAN architecture, [PERSON] et al. [38], a RF-GAN [PERSON]. [PERSON] et al. [15], a VSG-GAN [PERSON] et al. [39], and a DR-GAN [PERSON] et al. [40] generated realistic images with high fidelity. Using these images to train the classifiers improved their performance and generalization capacity. Using VAE in Diabetic Macular Edema disease, which is a more advanced manifestation of Diabetic Retinopathy, [PERSON] et al. [41] generated more realistic and high-resolution images than other GAN architectures. This made it possible to increase the dataset with the images generated, for training the classifier, and its efficiency improved. On the other hand, [PERSON] et al.