## 4 Discussion
Cone beam CT is necessary for assessing vertical bone height with respect to the lingual and buccal, but for general bone defect detection, no clear differences were observed between 2D and 3D images [6]. In dental radiology, a panoramic radiograph is typically used to screen patients during their initial visit to the clinic. This method provides information about the entire dentate, alveolar bone, and dentomaxillofacial anatomies of both upper and lower jaws with minimal time expense. Therefore, when a panoramic radiograph is taken, the assessment of the general condition of the periodontal bone is invaluable. The amount of alveolar bone loss and the severity of periodontitis require different therapeutic approaches [13]. Recent studies have proposed that both 2D and 3D X-rays are now being processed with technological innovations for deep learning in medical therapy [8]. [PERSON] et al. [26] compared the accuracy of measuring pocket depth using panoramic radiographs, intraoral radiographs, and actual probing depth. The study revealed that all images underestimated bone loss, but the difference between the measurements ranged within 1 mm. [PERSON] and [PERSON] reported their radiographic errors in 115 cases compared to gross measurement. The largest error between the actual bone height and the radiographic interpretation was 1.6 mm, indicating that the errors were within an acceptable range and justifiable [22]. Therefore, the stage interpreted based on a panoramic radiograph is representative in the clinical setting to initiate and help guide patients to receive treatment. We aimed to integrate the WHO CPI with dental panoramic images despite the challenges posed by the inherent low image sharpness and superimpositions in panoramic radiography. To achieve more reliable and reproducible results, we decided to classify moderate-to-severe periodontal bone loss as periodontitis. The development of a deep learning algorithm for detecting periodontal disease can be complex due to the unique nature of the oral cavity and dentition [2]. Factors such as the different numbers of teeth, quadrants of jaws, and various grading systems for periodontitis can result in different approaches to developing algorithms. Furthermore, the method of detection may differ in defining the region of the affected area. While some studies focus on examining each tooth axis and periodontal bone [5], others generate anchor boxes to define the region of each tooth with respect to the adjacent alveolar bone [21]. To create an efficient and reliable diagnostic dataset, grouping the dentition is often considered. Grouping the dentition into anterior, premolar, and molar regions or dividing it into four quadrants is generally used when planning periodontal flap operations [13]. Sectioning of the dentition is also applied to overcome difficulties in taking all varying axes of the tooth and to increase efficacy in producing a dataset. Therefore, for both efficient examination and high clinical applicability, we divided the whole dentition into anterior, premolar, and molar regions. According to this classification of regions of interest (ROIs), the best results were obtained through augmentation according to random rotation between 0 and 60 degrees. This suggests that the rotation angle of each tooth or ROI is under the range of 60 degrees from the vertical axis. Thus, this rotational parameter is suitable for building a robust model in response to augment diverse cases in a real population for the purpose of this study. In another experiment applying the image transformation of CLAHE (Contrast Limited Adaptive Histogram Equalization) to overcome the limitations of the study, we were unable to achieve better results. This may be due to the imbalance in the quality of individual or entire radiographic datasets. These innate shortcomings cannot be resolved by this study alone. To deal with higher resolution images, the use of more powerful GPUs with lots of memory might help to solve this limitation.