# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 5 Discussion and Conclusions
Factors such as body shape distribution, fat localization, and skin tone can vary significantly across different populations, potentially reducing prediction accuracy when the model is applied outside the original data domain. To improve the model's generalizability and applicability, future studies should aim to collect more diverse datasets that include a broader range of ethnic groups, body shapes, and demographic characteristics. In addition, applying domain adaptation techniques or style transfer-based approaches may help bridge the distribution gap between datasets and enhance model robustness across varying populations. Furthermore, BMI--a common index for classifying obesity [27]--was used as the conditional variable for data augmentation. However, while approximate body shapes can be predicted based on BMI, its accuracy may not be sufficient because it depends solely on height and weight [26; 47; 48]. Therefore, more accurate data could be generated if Dual-energy X-ray Absorptiometry (DXA) [49], the gold standard for body fat mass evaluation, is used as the conditional variable instead of BMI. Finally, since IZI based on body shape has not been performed previously, we had no reference to assess whether the error of our model is within an allowable range. Therefore, we had to rely on qualitative evaluations mimicking human judgment instead. The Pix2 Pix-based IZI model proposed herein enables patients to predict their appearance after liposuction based on their own body shape data. Building on this approach, we developed a deep learning-based prediction model trained on a paired dataset of pre- and post-liposuction RGB images. This model enables personalized appearance prediction from preoperative images, making the process more intuitive and accessible. To combat the lack of available data, we performed data augmentation using CcGAN, which employs continuous numerical values as conditional variables. The model's predictive performance was improved by generating body shape data based on BMI values as inputs. Further, by leveraging Pix2 Pix's reconstruction loss along with L1 loss and perceptual loss, our model outputs less blurry images as it minimizes both pixel-wise errors and deviations in the overall image structure. Because our model uses only RGB images rather than professional medical data, it can be utilized by the public to predict their post-surgery appearance without having to visit a hospital or any other specialized medical facility. Therefore, the proposed method can assist people in making informed decisions about whether to undergo liposuction. In future work, we plan to explore the applicability of advanced generative models such as StyleGAN3, diffusion-based models, and transformer-based conditional generators, which may better accommodate continuous condition variables such as BMI. Models such as DCAN, cGAN, PConv-GAN, StyleGAN, and StyleGAN2 were not adopted in this study because of specific limitations--such as segmentation-focused design, support for only categorical conditions, specialization in inpainting, reliance on latent space optimization, or the need for large-scale datasets--we recognize the potential value of these architectures in related domains. In future research, we aim to revisit these models by applying dataset expansion, customized training strategies, and architectural modifications to assess whether they can be effectively adapted to our paired RGB image setting. This may lead to improvements in image realism, training stability, and generalization across diverse populations. Ultimately, by integrating more advanced generative frameworks into clinical applications, we hope to enhance the robustness, scalability, and personalized utility of postoperative prediction systems in medical practice. [PERSON] was responsible for methodology, analysis, and writing. [PERSON] was responsible for data processing and analysis. [PERSON] was responsible for methodology. [PERSON] was responsible for methodology, review, and editing.