## 5 Conclusions  
In this study, an integrated model of the deep learning algorithms U-Net and YOLOv5 was developed for the classification of the stage of periodontitis using panoramic dental radiographs. The model was used to identify the stage of periodontitis as the PBL and CEJ boundaries were detected through the U-Net deep learning algorithm and as the tooth number and length were identified through the YOLOv5 algorithm. The model in this study was developed based on the IRB-approved data of patients with periodontitis at CBNUH and the data of an open database, AIHub. The model's performance was evaluated, and for this, a comparative analysis was performed using the data of assessment by dental specialists. The result indicated that the integrated model had an accuracy of 0.929, suggesting a high level of performance. Based on this, the model is expected to contribute to reducing the assessment errors from dental specialists using the dental radiographs of patients with periodontitis. It is also likely that the model will allow an objective assessment of alveolar bone loss, as well as treatment and prevention based on early diagnosis and the determination of the rate of disease progression. In the future, a more systematic comparison will be performed against a greater variety of models, while  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Recall (95\% CI)** & **Precision (95\% CI)** & **F1-Score (95\% CI)** \\ \hline
1 & 1.000 (1.000–1.000) & 0.962 (0.948–0.975) & 0.979 (0.97–0.989) \\ \hline
2 & 0.857 (0.85–0.862) & 0.941 (0.924–0.957) & 0.897 (0.886–0.907) \\ \hline
3 & 0.374 (0.364–0.384) & 0.772 (0.753–0.79) & 0.504 (0.483–0.525) \\ \hline
4 & 1.000 (1.000–1.000) & 0.253 (0.241–0.265) & 0.404 (0.388–0.419) \\ \hline Mean & 0.805 (0.799–0.811) & 0.732 (0.716–0.745) & 0.696 (0.681–0.709) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of the proposed framework for classifying periodontitis stages for each tooth.  
a system will be established for an easy and simple application to allow common use, to assist healthcare staff and patients in real clinical settings.  
Conceptualization: H.S.S. and V.K.; methodology: H.S.S. and V.K.; software: V.K.; validation: H.S.S. and E.J.C.; formal analysis: H.S.S. and V.K.; investigation: H.S.S. and J.S.P.; resources: H.S.S., E.-Y.L., S.-Y.K. and W.J.; data curation: H.S.S., S.-Y.K., W.J. and J.S.P.; writing--original draft preparation: H.S.S. and K.A.K.; writing--review and editing: K.A.K. and E.J.C.; visualization: K.A.K. and T.-G.K.; supervision: E.J.C. and K.A.K.; project administration: H.S.S. and E.-Y.L.; funding acquisition: T.-G.K. and K.A.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education No. 2020 RII1A101065199 and No. 2019R1F1A1051569.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Chungbuk National University Hospital (CBNUH 2021-11-011).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not available.  
The authors declare no conflict of interest.