# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 4 Discussion  
Many researchers have given attention to studying idiopathic scoliosis. Most existing methods use the Cobb angle method to assess the overall severity of scoliosis. Since the AASCE challenge considers SMAPE the only evaluation metric, most related studies on this challenging dataset only show the data of SMAPE for all angles. We believe that only providing the index of SMAPE is not enough for the auxiliary diagnosis of clinical medicine. Therefore, we provided more statistical indicators. Compared with state-of-the-art methods, our method has superiority in SMAPE. Compared with ground truth annotation, our automatic method could also provide clinically equivalent estimations in accuracy. Experiment results indicated that our method has strong reliability and reproducibility of Cobb angle measurements. Our future work will focus on classifying the disease severity according to the Cobb angle result. Furthermore, we will improve the current result to make it more suitable for cervical spondylosis diagnosis.  
In addition, it is necessary to discuss the influence of illumination on the experimental results to ensure the reliability of computer-aided diagnosis results. The impact of lighting problems on computer-vision tasks is expounded in [30]. In this work, the mean absolute error was 3.64\({}^{\circ}\) for the AASCE2019 dataset and 1.79\({}^{\circ}\) for the CS799 dataset. From analyzing the lighting situation in the dataset, one reason for the smaller average error on the CS799 dataset may be because the image lighting situation in CS799 is more stable than that in AASCE2019. Most of the images in AASCE2019 are shown in Figure 1c,d, with significant changes in illumination. While the CS799 images are shown in Figure 3, the changes in illumination are not obvious. For these two different tasks, the goal is the same. Their goal is to find the optimal algorithm to reduce the error of Cobb angle estimation. For AASCE2019, another requirement is that the algorithm should be robust against noise and lighting changes; this means that the AASCE2019 dataset has higher requirements for the algorithm.  
We demonstrated the effectiveness of each module through ablation experiments on the CS799 dataset. At the same time, the method was applied to the AASCE2019 dataset, which proved that the method is still robust to illumination and noise changes. However, there are also some limitations in our work. First, our method was only implemented on frontal images of the public database, with no involvement in the complex conditions of clinical practice. In addition, sagittal alignment also plays an increasingly important part in clinical outcomes, and researchers should pay more attention to it. Although our experiments were limited to frontal spine images, our method can overcome huge variations and high ambiguities of the public dataset. The performance of our model implies its robust  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pre-Trained** & **SMAPE (\%)** & **MAD (SD)** & **Confidence** & **R (\(p\)-Value)** & **MSE** \\ \hline P & 11.07 & 1.90\({}^{\circ}\) (1.89\({}^{\circ}\)) & 5.60\({}^{\circ}\) & 0.9598 (\(<\)10\({}^{-6}\)) & 5.25 \\ - & 11.06 & 1.79\({}^{\circ}\) (1.86\({}^{\circ}\)) & 5.45\({}^{\circ}\) & 0.9619 (\(<\)10\({}^{-6}\)) & 5.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the model with and without the pre-training model.  
adaptability to the high-definition in-house images, which will be performed in future research. Second, our automatic method may occasionally yield an incorrect result (see Case 5 and Case 6 in Figure 3). We will continue to optimize the network model to narrow the confidence interval. Third, our methods have not yet provided classification results for cervical spondylosis and scoliosis severity. A professional medical analysis and diagnosis results must be combined with professional medical field knowledge, which is also a shortcoming of our method.