## 3 Discussion  
The integration of artificial intelligence (AI) into periodontology, particularly through neural networks, has marked a significant leap in the accuracy and efficiency of diagnosing periodontal diseases. AI systems have shown remarkable capabilities in detecting alveolar bone loss (ABL) and clinical attachment loss (CAL), two critical parameters for diagnosing and staging periodontal conditions [1,4,51]. The success of AI models like convolutional neural networks (CNNs), hybrid neural networks, and generative adversarial networks (GANs) reflects the transformative potential of these tools in modern dental practice. For instance, [PERSON] et al. [29] developed a seven-layer CNN that achieved 81% accuracy, sensitivity, and specificity in detecting ABL from panoramic radiographs, comparable to experienced dentists (76%). Similarly, [PERSON] et al. [30] introduced DeNTNet, which employs transfer learning and Grad-CAM interpretability, achieving an F1 score of 0.75, outperforming clinicians' score of 0.69, and demonstrating superior sensitivity and specificity in complex imaging scenarios. The [PERSON] et al. [32] study on Deetal-Perio demonstrated its capability to segment and number teeth while calculating ABL using Mask R-CNN, achieving F1 scores of 0.894 and 0.820 across two datasets. Its integration of clinical interpretability further highlights its utility in assisting clinicians. [PERSON] et al. [33] highlighted YOLOv5's ability to perform localized segmentation, excelling at identifying bone loss in specific dental regions such as incisors, with high sensitivity and precision. In addition to CNN-based approaches, hybrid models have shown promise in combining the strengths of various AI methods. [PERSON] et al. [45] introduced a hybrid DL system that achieved 92% pixel accuracy in identifying bone levels and staging periodontitis, showing strong correlations with expert diagnoses. These models, while effective overall, faced challenges staging molars and incisors due to anatomical complexity. Advanced imaging techniques were further explored in studies by [PERSON] et al. [35], which used super-resolution CNNs to enhance radiograph visual clarity, improving bone loss identification and boosting diagnostic confidence. [PERSON] et al. [39] demonstrated a hybrid model combining YOLOv5, VGG-16, and U-Net, achieving up to 97% accuracy in detecting radiographic bone loss, surpassing traditional clinician accuracy. However, challenges remain, particularly in addressing anatomical variability, molars with complex root structures, and vertical bone loss. Studies such as [PERSON] et al. [40] highlighted reduced performance in detecting vertical bone loss (AUC = 0.733) compared to horizontal loss (AUC = 0.910), emphasizing the need for robust datasets and 3D imaging integration. Similarly, [PERSON] et al. [37] reported moderate reliability (ICC = 0.601) in ABL estimation, with improved performance on non-molar teeth. These results demonstrate the strengths and limitations of artificial intelligence in periodontal diagnostics at present. Future efforts should prioritize creating larger, more diverse datasets, incorporating 3D imaging modalities such as Cone Beam Computer Tomography (CBCT), and refining algorithms for complex cases. In fact, studies that apply deep learning algorithms on CBCTs are not widespread because this method of investigation is not indicated in the diagnosis of periodontal disease, which instead makes use of 2D imaging techniques, such as endoral bitewing [1,51,52]. Expanding interpretability tools and integrating clinical parameters will further enhance trust and usability among clinicians. The path forward involves several key developments. First, expanding datasets to include diverse patient demographics, bone and dental anatomical complexities, and advanced imaging modalitieswill be critical for refining AI algorithms.