## 4 Discussion
### Application of AI in Diagnosis and Determining the Need of OGS  
OGS is considered when the occlusion that can potentially be achieved with orthodontic treatment alone is inadequate, rendering it impossible to resolve the patients' chief complaint with a less invasive procedure. To achieve the best treatment outcomes, precise diagnosis and sound decision making is very important. Various studies have described the application of AI in the diagnosis of OGS. [PERSON] et al. reported on applying machine learning model for determining the need for OGS using landmarks on cephalometric radiographs. This AI-based model demonstrated excellent results with 96% success rate in diagnosing cases that require surgical and non-surgical treatment and 91% accuracy in deciding on the type of surgery and the need for extraction. The limitation of this study was that the authors had excluded cases with skeletal asymmetry [33]. Another study conducted by [PERSON] et al. reported on using machine leaning model for automated diagnosis of cases requiring OGS using 3D images. This model demonstrated outstanding results with 95.5% sensitivity and 95.2% specificity in diagnosing OGS and mean accuracy of 1.1 \(\pm\) 0.3 mm in simulating the surgical outcomes. However, the training of this model was limited with a small number of data sets, and it had also included volunteers indicated for mild OGS \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Outcome** & **Inconsistency** & **Indirectness** & **Imprecision** & **Risk of Bias** &
\begin{tabular}{c} **Strength of** \\ **Evidence** \\ \end{tabular} \\ \hline Application of AI diagnosis and determining the need of OGS [33; 34; 35; 36; 37] & Not Present & Not Present & Not Present & Not Present & \(\oplus\oplus\oplus\oplus\) \\ Application of AI in differential diagnosis of OGS [42] & Not Present & Not Present & Not Present & Not Present & \(\oplus\oplus\oplus\) \\ Application of AI for predicting the post-operative facial profiles and facial symmetry [25; 26; 27; 28; 29; 30; 31; 32] & Not Present & Not Present & Not Present & \(\oplus\oplus\oplus\) \\ Application of AI for planning OGS [39; 40] & Not Present & Not Present & Not Present & Present & \(\oplus\oplus\)\(\odot\) \\ Application of segmentation of maxillofacial structures for OGS [41] & Not Present & Not Present & Not Present & Not Present & \(\oplus\oplus\)\(\odot\) \\ Application of AI for predicting blood loss prior to OGS [38] & Not Present & Not Present & Not Present & Present & \(\oplus\oplus\)\(\odot\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Assessment of Strength of Evidence. for validation. Performance of this model can be enhanced by integrating shape data and electronic medical records [34]. [PERSON] et al. in his study reported of 4 AI-based models ResNet-18, 34, 50 and 101 with different depths of neural networks in diagnosis of OGS. The average success rate of these AI-based models was 93.80%, 93.60%, 91.13%, and 91.33%, respectively. ResNet-18 outperformed among the four models with AUC of 0.979. These findings confirm that the linear structure demonstrated better performance in comparison with bottleneck structure. It also confirms that for using the linear structure effectively the neural networks should not be too deep. However, in this study the data was lacking multicenter representation, as it was obtained from one single center [37]. In their study, [PERSON] et al. reported on using a machine learning model to predict the need for OGS in patients who were treated for unilateral cleft lip and palate using lateral cephalogram variables. This AI model demonstrated an accuracy of 87.4% in predicting the need for OGS. In order to avoid selection bias, the authors included patients from a single ethnicity, cephalometric data from a younger age group of 6.3 years, patients requiring the same treatment protocol and examinations carried out by one orthodontist. A limitation of this study was the small number of sample size from one center [35]. [PERSON] et al.