## 4 Discussion
The study demonstrated a precision of 0.86 and recall of 0.84 in detecting periodontitis using the largest dataset comprising of 48,996 ROIs, which is consistent with the findings of [PERSON]'s study that showed an average precision of 0.81 and recall of 0.80 [21]. These results indicate that the regional grouping of teeth is a reliable approach for detecting periodontal bone loss according to the new criteria proposed at the 2017 World Workshop 4. Furthermore, the high-performance index values over 0.84 for both the edentulous and tooth areas indicate that the trained network can effectively distinguish and recognize between the tooth and alveolar bone (edentulous) areas. Additionally, the similar detection performance of the three classes regardless of the tooth's location suggests that the learned network can effectively detect periodontitis, regardless of the tooth's position. The results showed high consistency and reproducibility with an inter- and intra-examiner correlation coefficient (ICC) value of 0.94. The proposed deep learning model demonstrated high performance indices (precision, recall, F1-score, Area Under the Curve) in detecting periodontally compromised and healthy teeth. The confusion matrix and Area Under the Curve showed a high level of correct classification. Overall, the study demonstrated the potential of deep learning models in accurately detecting periodontal disease from dental radiographs, but further improvements are needed to reduce misclassifications and increase the accuracy of the model. Incorporating panoramic radiographs into the initial screening for periodontal disease may lead to faster diagnosis. Our deep learning program can save both healthcare providers and patients time and cost during clinic visits. Further development of the algorithm into a simple cellphone application can also be considered for the interest of public health. Individuals with limited time for dental visits can take a panoramic radiograph and input it into the application to assess their periodontal disease status. This approach may also increase patients' awareness of their current status of periodontal disease since they have access to their own radiographic images. As the public's dental intelligence grows, supplementing deep learning algorithms with public access can aid in the detection of periodontitis and alveolar bone loss, providing a reliable diagnostic tool for dentistry. The limitation of this study is related to the imaging properties of panoramic radiography, which may have an impact on the diagnostic performance for assessing periodontitis due to the innate low image sharpness and superimpositions [22]. Since periodontitis is a complex disease that requires attention to multiple aspects of bony defects, accuracy may also be enhanced by training the AI with the combined training of panoramic radiographs, cone beam CT scans, or intraoral radiographs. Future studies can improve diagnostic accuracy by incorporating these modalities of radiographic examinations. Additionally, supplementing clinically measured levels of attachment loss will enable the AI algorithm to produce more precise and valid diagnostic information.