## 3 Results and Discussion
As shown in Table 1 and Figure 5B-D, from the aspect of the width and symmetry of the alveolar cleft between the design results of the appliance (this study) and the original maxillary model, the width of the alveolar cleft (PP') in the design results is significantly reduced. For the samples with high LS and MS, such as No. 3, No. 8, No. 9, and No. 10, the PNAM algorithm designed in this paper can significantly improve its symmetry. For samples with low LS and high MS, such as samples 1 and 2, the algorithm can also greatly improve its symmetry. For samples with low LS and low MS, such as No. 4, No. 5, No. 6, No. 7, and No. 11, with the exception offor samples No. 4 and No. 5, the symmetry of samples has been significantly improved. For samples No. 4 and No. 5, the algorithm designed in this paper can also improve the similarity in one of length or morphology, which will not lead to the phenomenon of symmetry imbalance, equivalent to a slight increase in the symmetry of samples. Paired t-tests were used to study the difference between the results of the original model and the results after the design. The confidence level \(p\) was 0.95. From Table 2, it can be seen that there are a total of 3 sets of matching data, PP' shows a difference (t = 8.330, \(p\) = 0.000), MS shows a difference (t = 3.250, \(p\) = 0.009), and the difference of LS is not obvious (t = 1.559, \(p\) = 0.150), but from the mean and variance, it can be seen that there is a certain improvement. By analyzing the width and symmetry of the alveolar cleft between the design results of the appliance and the original maxillary model, it is found that the algorithm model designed in this paper has a significant improvement in both indicators. Therefore, the algorithm model designed in this paper can be effectively applied to correct unilateral complete cleft lip and palate in theory and can achieve good results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Original Model Results**} & \multicolumn{4}{c}{**Clinical Correction Results**} & \multicolumn{4}{c}{**Design Result**} \\ \cline{2-10}  & **PP’/mm** & **LS** & **MS** & **PP’/mm** & **LS** & **MS** & **PP’/mm** & **LS** & **MS** \\ \hline
1 & 6.13 & 0.29 & 0.90 & 1.18 & 0.06 & 0.18 & 3.52 & 0.24 & 0.11 \\
2 & 8.73 & 0.34 & 0.58 & 4.55 & 0.11 & 0.17 & 4.75 & 0.02 & 0.10 \\
3 & 9.78 & 0.40 & 0.86 & 5.47 & 0.07 & 0.34 & 5.00 & 0.02 & 0.04 \\
4 & 5.94 & 0.35 & 0.04 & 5.52 & 0.015 & 0.04 & 3.38 & 0.04 & 0.37 \\
5 & 8.87 & 0.08 & 0.19 & 4.72 & 0.03 & 0.42 & 1.24 & 0.25 & 0.57 \\
6 & 9.39 & 0.12 & 0.03 & 2.92 & 0.21 & 0.05 & 5.11 & 0.12 & 0.34 \\
7 & 11.67 & 0.39 & 0.37 & 3.70 & 0.20 & 0.31 & 7.39 & 0.16 & 0.50 \\
8 & 12.52 & 0.76 & 2.13 & 3.49 & 0.22 & 0.38 & 3.14 & 0.071 & 0.39 \\
9 & 7.82 & 0.42 & 0.46 & 2.18 & 0.07 & 0.19 & 2.48 & 0.29 & 0.48 \\
10 & 10.84 & 0.45 & 1.26 & 4.43 & 0.04 & 0.29 & 4.93 & 0.28 & 0.17 \\
11 & 10.97 & 0.38 & 0.21 & 8.60 & 0.16 & 0.29 & 4.23 & 0.03 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of crack width and original model results, clinical correction results(clinic), and design result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Evaluating**} & \multicolumn{1}{c}{**Original Model**} & \multicolumn{1}{c}{**Design Result**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**_p_} \\ \multicolumn{1}{c}{**Indicator**} & **Results (m \(\pm\) s)** & & & & \\ \hline PP’/mm & 9.33 \(\pm\) 2.13 & 4.11 \(\pm\) 1.62 & 8.330 & 0.0000 \\ MS & 0.36 \(\pm\) 0.18 & 0.14 \(\pm\) 0.11 & 3.250 & 0.009 \\ LS & 0.64 \(\pm\) 0.63 & 0.31 \(\pm\) 0.18 & 1.559 & 0.150 \\ \hline \hlineAlthough good results can be achieved in theory, they need to be compared to clinical data, to apply the proposed algorithm model to clinical practice. Based on this, 11 samples were corrected, and the feature points of the model after correction were extracted and compared with the algorithm model designed in this paper.