## 3 Experiments and Results
### Determination of Tooth Stage through U-Net and YOLOv5 Integration  
Classification of the PBL by Percentage Rate Analysis  
To detect the PBL and CEJ boundaries, a model of U-Net was developed, and to detect the individual tooth number, a model of YOLOv5 was used; then, an integrated model was built. To classify the periodontitis stage, the PBL and CEJ boundaries were detected via  
Figure 8: Tooth length detection using a YOLOv5 model.  
Figure 7: Automatic tooth numbering using a YOLOv5 model.  
U-Net, and the two intersections were used to calculate the lengths of the PBL and CEJ and intersections for the tooth (Figures 9 and 10). The ratio of the RBL to the tooth was defined as the ratio of the PBL and CEJ intersection lengths. Based on the criteria of the 2017 World Workshop, the level of alveolar bone loss was automatically classified such that the stage of periodontitis could be identified. For periodontitis staging, an RBL of <15% indicates S1, an RBL of 15%-33% indicates S2, and an RBL of \(\geq\)33% indicates S3; S4 corresponds to cases where the sum of tooth loss and implant is \(\geq\)4 in identical conditions as S3. Using these criteria, the stage of each tooth was detected, and the result is shown in Figure 11.  
Figure 10: PBL and CEJ intersection with the tooth main axis.  
Figure 9: PBL and CEJ boundaries detection using U-Net models.  
The performance of the integrated deep learning model was evaluated upon comparison with the results of dental specialists. The comparative analysis applied the data of stage classification by specialists as 10 test datasets to the integrated model developed in this study based on U-Net and YOLOv5 for the identification of the stage of periodontitis. From these 10 panoramic radiographs, dental specialists extracted 140 tooth objects and evaluated them one by one before comparing them with the predictions of our framework. The distribution of the test dataset is mostly on the maxillary molar side in stage 2, and stage 1 shows the highest frequency in the rest of the test dataset. Specifically, if you look at the distribution of 140 objects in the test dataset, it is as follows. Stage 1 shows 97 objects; stage 2 has 34; stage 3 has 8; stage 4 has 1; so, the data imbalance is very severe. These issues should be addressed through additional data collection in the future. The test dataset was used to evaluate the proposed framework, and the results are presented in Table 2.  
The accuracy of the integrated model was 0.928; the recall, precision, and F1-score are presented in Table 3. The result showed that the mean recall, precision, and F1-score were the highest for S1, while the higher values of precision and F1-score indicated the lower stages. In addition, the recall indicated a high level of accuracy for S1 and S4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Stage Count of Test Dataset** & **Stage Count of Prediction** \\ \hline
1 & 97 & 101 \\ \hline
2 & 34 & 31 \\ \hline
3 & 8 & 4 \\ \hline
4 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of test dataset and framework prediction results.  
Figure 11: Periodontal stage classification according to the RBL ratio using the PBL and CEJ length intersection.