## 4 Discussion
### Scoliosis Screening System Combined with AI
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Aim** & **Dataset (n)** & **AI models** & **Evaluation** & **Results** & **Year** & **Reference** \\ \hline \multirow{8}{*}{[PERSON] et al.} & To predict the 3D radiographic & & & & The surgical factors, upper and lower instrumented vertebrae, and the operating surgeon operating surgeon were important & \multirow{8}{*}{2021} & \multirow{8}{*}{[12]} \\  & & & & & (Accuracy = 75\% and \\  & To predict the 3D & & & & (Max AUC = 0.86) & \\  & radiographic & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & & & & & (Max AUC = 0.86) & \\ \hline \multirow{8}{*}{[PERSON] et al.} & To propose a & & & & Age identification using X-ray images (RAE for 68 months (1.37\%), 84 months (2.55\%) and 127 months (0.87\%)) & \multirow{8}{*}{2021} & \multirow{8}{*}{[13]} \\  & machine learning & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & algorithm in & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & order to study & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & patient-specific & & & & (Max AUC = 0.86) & \\ \cline{1-1}  & AIS curve & & & & (Max AUC = 0.86) & \\ \hline \multirow{8}{*}{[PERSON] et al.} & \multicolumn{2}{c}{Scoliosis} & \multirow{8}{*}{800} & \multirow{8}{*}{Convolutional neural network} & \multirow{8}{*}{Accuracy} & \multicolumn{1}{c}{Absolute Cobb} & \multirow{8}{*}{2020} & \multirow{8}{*}{[11]} \\  & prediction by & & & & & (Max AUC, & \\ \cline{1-1}  & using deep & & & & (Max AUC, & (Max AUC, & (0.946), sensitivity \\ \cline{1-1}  & learning & & & & (Max AUC, & (87.5\%), specificity & \\ \cline{1-1}  & & & & & (Max AUC, & (83.5\%), and PPV & \\ \cline{1-1}  & & curve \(\geq\)20\({}^{\circ}\) & & & & (86.2\%)) & \\ \hline \multirow{8}{*}{[PERSON] et al.} & \multicolumn{2}{c}{Automatic} & \multirow{8}{*}{42} & \multirow{8}{*}{Support vector} & \multirow{8}{*}{Accuracy, sensitivity, and specificity} & \multirow{8}{*}{2018} & \multirow{8}{*}{[10]} \\  & & & & & & (Max AUC, & \\ \cline{1-1}  & & & & & (Max AUC, & (0.946), sensitivity & \\ \cline{1-1}  & & & & (Max AUC, & (87.5\%), specificity & \\ \cline{1-1}  & & & & & (Max AUC, & (83.5\%), and PPV & \\ \cline{1-1}  & & & & & (86.2\%)) & \\ \hline \multirow{8}{*}{This study} & To analyze & & & & (Max AUC, & (Max AUC, & (Max AUC, & (0.86), sensitivity \\ \cline{1-1}  & feature & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & importance to & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & postural & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & deformity & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & parameters & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & extracted from a & & & & (Max AUC, & (0.86), sensitivity & \\ \cline{1-1}  & CVPAS & & & & (Max AUC, & (0.86), sensitivity & \\ \hline \hline \end{tabular} Note: 3D: three-dimensional; AIS: adolescent idiopathic scoliosis; AUC: area under the curve; ROC: receiver operating characteristics; RAE: relative approximation error; RCNN: region-based convolutional neural network; ResNet: residual neural network; PPV: positive predictive value; CVPAS: computer vision-based posture analysis; system; XGBoost: Extreme gradient boosting; SHAP: Shapley additive explanations; LIME: local interpretable model-agnostic explanations; XAI: explainable artificial intelligence.  
\end{table} 
Table 5: AI studies for scoliosis or postural deformity analysis.