# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 5 Discussion and Conclusions
This inconsistency in arm placement led to ambiguity in the boundary between the abdomen and the arms, especially causing loss of contour clarity or distortion around the abdominal area. Such variations disrupted the model's ability to learn the structural features of the abdomen consistently and ultimately resulted in blurring or smearing artifacts in the predicted images. To address this issue, future data collection protocols should include explicit pose guidelines, including the angle of the arms, the width between the shoulders, and the distance between the arms and torso. Providing standardized posture instructions to participants prior to image capture will help ensure greater consistency across samples, thereby enabling the model to more accurately learn and reproduce key body regions, particularly the abdominal area. This study also faced a limitation from the use of only a single-view input--the left lateral image of the upper body. While this viewpoint effectively captures changes in the abdominal and flank areas, it is insufficient for ensuring anatomical consistency from other angles, such as frontal or dorsal views. This limitation may restrict the model's ability to learn comprehensive morphological changes. Future studies are expected to incorporate multi-view image inputs, including front, side, and back views, to enable more coherent and spatially consistent predictions of body contour changes. Furthermore, conducting quantitative validations for each viewpoint will help improve anatomical fidelity and enhance the generalizability of the model. A further limitation was observed in patients with relatively low BMI; in such cases, the physical changes before and after liposuction tend to be subtle and less visually noticeable. As a result, the model sometimes failed to capture these minimal differences, generating output images with little to no perceived change. This suggests that the model's performance may be less effective in cases where the visual impact of liposuction is inherently limited, which poses a challenge for prediction based on subtle morphological cues. Another critical factor that was not explicitly addressed in this study is the potential variation in fat distribution patterns between genders. Numerous clinical and physiological studies have shown that males and females tend to accumulate and reduce fat differently--males often in the abdominal area and females more evenly distributed across the hips, thighs, and waist. Since our model was trained using images of patients without explicitly separating by gender, these biological differences may influence both the training dynamics and prediction accuracy. Particularly, morphological changes that are subtle or anatomically different because of gender-specific fat localization may not be fully captured by a single unified model. Therefore, it is important to acknowledge that the applicability of the proposed model may vary depending on gender, and future research should consider stratifying training data or building gender-specific models to enhance accuracy and interpretability. Incorporating such considerations will also help define the boundaries of the model's generalization and clarify its clinical utility. In addition, the dataset used in this study does not include patients with clinically extreme BMI values (i.e., BMI \(\geq\) 40 or BMI < 16); the model's generalizability to such cases could not be evaluated. Therefore, future research should involve the collection of data from patients with pathological obesity or severely underweight to more rigorously assess the clinical applicability and robustness of the proposed model. Moreover, the training dataset used in this study was composed entirely of Korean individuals. While this allows the model to learn the body characteristics and liposuction-related changes typical of this population, it also raises concerns about the model's ability to generalize to individuals from other ethnicities and body types.