## 4 Discussion
### Limitations of the Study  
Although the results of this study show a sufficiently high degree of accuracy, several limitations exist. First, it is unclear whether the regression and classification models created look at the vertebrae and determine their angles in the same manner as humans. Not only pseudo-created lumbar spine X-ray images such as those used in this study but also X-ray images contain a variety of information in addition to the vertebral bodies, so it is unclear which part of the body the models are looking at to estimate the angle. Next, while this study used lumbar spine X-ray images pseudo-generated from CT images for model creation, we acknowledge the importance of incorporating real-world X-ray images for a more comprehensive evaluation of the model's performance. Future studies should validate the accuracy of the model using actual lumbar spine X-ray images to confirm its applicability in clinical settings. As in other studies using CNNs [11; 12; 13; 14; 16; 17], we believe that the accuracy of the angle estimation model may be improved by increasing the number of training images and by using other models and parameters. In particular, in terms of increasing the number of training images, by using the method of pseudo-creating lumbar spine X-ray images from CT images in this study, it is possible to obtain lumbar spine X-ray images from any dataset as long as the lumbar spine is included in the CT image range. We believe that this will solve one of the major problems in CNN research, namely, the lack of training data. However, it is not easy to collect a large number of datasets that include the lumbar spine in the CT scan area. In addition, the lumbar spine X-ray images pseudo-generated from CT images do not exactly match the actual X-ray images, so there are limitations to learning using pseudo images. Effective data collection and learning methods need to be considered while taking these points into account. The significance of this study lies in the fact that the CNN was trained using pseudo X-ray images of the lumbar spine taken at arbitrary angles from CT images. We have not been able to confirm any studies that have created CNNs using pseudo X-ray images, indicating the uniqueness of this study. In addition, no previous study has been confirmed that regressively analyzes the lumbar spine according to angle or classifies the lumbar spine into classes by angle. Therefore, we believe that the present results suggest the usefulness of the CNN created using lumbar spine X-ray images pseudo-generated from CT images for angle estimation in lumbar spine oblique images using deep learning techniques. However, this study is only an initial investigation, and the aforementioned limitations exist. Inthe future, while taking these limitations into account, it will be necessary to verify the method using a larger dataset to explore other models and parameters and to evaluate the method using actual X-ray images. It is also important to further validate the usefulness of the method proposed in this study by applying it to X-ray images of other sites. To further enhance our research perspective, incorporating advanced techniques in image processing, segmentation, and simulation of spine or bone is essential. Notable studies such as CSR-Net for scaphoid fracture segmentation [32], wearable technologies for monitoring spinal movement [33], adversarial models for bone segmentation in CT images [34], and neuroprosthetic approaches for managing hemodynamics post-spinal injury [35] provide valuable insight for future implementations. The clinical significance of this study is that it is expected to optimize the angle and improve reproducibility in lumbar oblique imaging. Lumbar oblique radiography plays an important role in the diagnosis of lumbar spondylolysis and spondylolisthesis, but it must be performed at an appropriate angle. However, at present, determination of the imaging angle relies heavily on the radiologist's experience and intuition, and it lacks reproducibility and consistency.