# Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets
## 2 Related Works
### Synthetic Data Generation  
Research on dataset synthesis has been actively pursued to overcome the limited availability of medical data. Synthetic data generation plays an important role, especially in the medical imaging field, where various techniques have been applied. Figure 1 illustrates the methods used for synthetic data generation.  
Traditional methods for synthetic data generation primarily expand datasets through image transformation. Common methods include rotation, translation, scaling, inversion, and brightness and contrast adjustment. These methods enhance the robustness of the model to various transformations while preserving the meaning of the original image. For instance, traditional image synthesis techniques were applied in specific studies to enhance the performance of classification models. Studies that employed traditional synthetic data-generation techniques, such as geometric transformations and color adjustments, have demonstrated significant improvements in the accuracy of classification models [31, 32, 33].  
Deep-learning-based synthetic data-generation techniques generate new images using models such as generative adversarial networks (GANs). These methods create more diverse and realistic data by learning patterns from the original dataset. As proposed by [PERSON] et al., GANs have revolutionized synthetic data generation [34]. GANs produce new high-quality data through competitive learning between two neural networks: generator and discriminator. CycleGAN, a variation of GAN, enables image conversion between domains and offers various potential applications, such as transforming data from other diseases into PI data [35].  
Synthetic data generation is highly significant in medical imaging due to data scarcity and labeling challenges. Recent studies have maximized model performance by combining various synthetic data-generation techniques. For example, research has demonstrated improved accuracy in medical image analysis by integrating traditional synthetic data-generation methods with GANs. The application of these techniques to echocardiography and computed tomography (CT) scan data significantly enhances the accuracy and robustness of the models [36, 37]. Furthermore, a study utilizing synthetic rare disease data with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Feature** & **Model** & **Accuracy** & **Class** \\ \hline [PERSON] et al., 2020 [25] & Image & VGG-Face, ResNet, SVM & 99\% & Burn, PI * \\ [PERSON] et al., 2021 [26] & Image & Logistic Regression & 76\% & Stage 1 PI, Stage 2 PI, Stage 3 PI, Stage 4 PI, unstageable PI \\ [PERSON] et al., 2022 [27] & Bounding Box & ResNet152 & 93\% & Stage 1 PI, Stage 2 PI, Stage 3 PI, Stage 4 PI \\ [PERSON] et al., 2022 [28] & Image & Inception-ResNet-V2 & 95\% & extensive necrosis, moderate necrosis, limited necrosis \\ [PERSON] et al., 2023 [29] & Bounding Box & Faster R-CNN & 62\% & Stage 1 PI, Stage 2 PI, Stage 3 PI, Stage 4 PI, unstageable PI, deep tissue PI \\ [PERSON] et al., 2024 [30] & Bounding Box & semi-two-stream CNN & 93\% & Stage 1 PI, Stage 2 PI, Stage 3 PI, Stage 4 PI \\ \hline \hline \end{tabular}
* PI = Pressure Injury.  
\end{table}
Table 1: Existing studies on Pressure Injury (PI) assessments.  
Figure 1: Traditional and deep-learning-based synthetic data-generation method in PI.  
GANs applied to CT and MRI data demonstrated substantial improvement in the model's prediction performance [38].  
Synthetic data generation using diffusion models has recently attracted significant attention. Diffusion models generate new images by gradually reducing noise and creating high-quality synthetic data. [PERSON] et al. [39] demonstrated that diffusion models can generate meaningful synthetic data from skin disease datasets.