## 4 Discussion
### Comparison with Other Studies  
As for angle prediction using CNNs in the vertebral field, many studies [21; 22; 23] have been conducted to estimate the Cobb angle in patients with scoliosis. The Cobb angle represents the angle of posterior curvature of the spine and generally refers to the angle that indicates the degree of posterior curvature of the thoracic spine. This angle is usually quantified by measuring the curvature between two specific vertebrae of the thoracic spine. With reference to these results, it can be said that the accuracy of the regression model created in this study in predicting the angle at which lumbar oblique image was taken was sufficiently high. The reason why is that the accuracy of the present study was higher than that of another study [24]. According to the paper, the RMSE for predicting the Cobb angle was 5.48 degrees on average, and the correlation coefficient between the actual Cobb angle and the predicted Cobb angle was 0.91. This is thought to be because the study used images of a human bent forward taken from multiple angles to predict the Cobb angle, and the images used were not as clear as X-ray images. On the other hand, when the accuracy of this study was compared with that of another study [25] that estimated the Cobb angle using a model similar to this study, which was created using X-ray images of the vertebral body, the accuracy of this study was relatively lower. In the external validation dataset of the model for predicting the Cobb angle, the RMSE value was 1.5\({}^{\circ}\), and although there was no direct mention of the r value, the agreement between the model and the Cobb angle was shown to be very high, with an intraclass correlation coefficient (ICC) of 0.996. This may be due to the difference in the number of data used, but it is also thought to be due to the difference in the dimension of the angle being predicted. Specifically, this is because the Cobb angle estimation estimates a two-dimensional angle in terms of curvature between two specific vertebrae, whereas the lumbar oblique image angle estimation estimates an angle that can be described as a \"degree of rotation\" rather than a degree of vertebral flexion, etc., in other words, a three-dimensional angle. We believe this difference is the reason why the accuracy of this study was relatively low compared to the accuracy of other studies that estimated Cobb angles. Similar to other regression methods using deep learning in medical imaging [26; 27], the significant positive correlation obtained suggested that the predictor was reliable. In Table 3, the observed variations in MSE and RMSE values for folds 2 and 5 invite cautious speculation regarding their cause. These disparities might reflect distinctive characteristics of the data subsets in these folds or specific interactions between the model and these subsets. Notably, while fold 2 shows larger error magnitudes, as indicated by the higher MSE and RMSE values, it has a lower correlation coefficient compared to fold 5, suggesting that errors in fold 5, although smaller in magnitude, could be more variable or less consistent. It is plausible that such differences are attributed to the stochastic nature of the data or to unique patterns not yet fully understood. Increasing the number of cases might enable a more comprehensive evaluation of the model's performance across varying data conditions. Our study effectively demonstrates the utility of a specific pre-trained CNN model for medical imaging via transfer learning. However, reliance on this singular CNN architecture, while efficient, limits the exploration of newer models that may offer enhanced performance with fewer resources [28; 29]. Future research should consider a broader array of models to improve generalizability and performance in diverse imagingcontexts [30; 31]. This study lays the groundwork for further exploration into the capabilities of CNNs and their alternatives in medical imaging.