# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 4 Discussion
The use of a wide variety of metrics in the studies analyzed only reflects the variety of evaluations and validations that can be made to the results in order to obtain more reliable results. A deep understanding of the metrics used to evaluate synthetic data is essential, both in terms of how they work and the interpretability of the results. Combining quantitative and qualitative metrics, including evaluation by retinal experts, can increase the reliability of the analyses. In addition, understanding the criteria used by experts to differentiate between real and synthetic data can offer a more comprehensive perspective and strengthen the robustness of the conclusions drawn. Although the metrics analyzed in the studies are widely used, they may not be sufficient for a complete assessment of the quality of synthetic images. The inclusion of additional metrics, such as IS and PSNR, could provide more information, despite the increased complexity of the analysis. Still, expert evaluation remains essential, especially considering that GANs are not yet fully optimized. While quantitative metrics offer objective data on the realism of images, complementary methods--such as perceptual analysis by experts or validation based on clinical tasks (e.g., impact on the performance of classifiers trained with synthetic data)--can increase confidence in the clinical applicability of these images. The results of this systematic review reinforce the ability of generative models to create synthetic data with realistic characteristics, often indistinguishable from real images, as stated by several authors. In some cases, the synthetic data presented have an even higher resolution and degree of realism compared to the original data. Notably, in evaluations conducted by ophthalmic specialists, synthetic and real data were frequently indistinguishable. These results were consistent across various retinal diseases, including inherited retinal disorders, with no clear evidence suggesting that specific conditions yielded superior results. However, data availability remains a critical factor. More prevalent diseases, such as Diabetic Retinopathy, benefit from larger datasets, whereas rare IRDs often face challenges due to data scarcity. Despite this limitation, several studies have highlighted the ability of generative models to produce realistic, high-quality FAF images, even when trained on small datasets, reinforcing their potential to mitigate data scarcity in these conditions. Furthermore, studies that integrated synthetic data with real data to train classifiers reported improved model accuracy and generalization capabilities. This enhancement is largely attributed to the increased diversity of training data, suggesting that synthetic FAF images could play a crucial role in optimizing deep learning models for diagnosing hereditary retinal diseases. An important aspect to consider when using generative models for data augmentation is their computational efficiency. Among the 32 studies analyzed, different GAN architectures were used, each with different computational requirements to generate synthetic retinal images. Some of the studies analyzed only refer to the execution environment of the architectures, without referring to or comparing the computational cost that this required. However, more complex models, such as StyleGAN, usually require high-performance GPUs and long training times (can generate images with short training periods), to produce high-fidelity images, while simpler architectures, such as DCGAN, may be more efficient from a computational point of view, but potentially generate lower quality samples. In order to apply these methods in the real world, to generate synthetic data for training deep learning classifiers, the need and quality of the data must always be taken into account, as opposed to the resources available to generate these data.