## 2 Materials and Methods
### Convolutional Neural Network (CNN)
[37] employed a CNN model to estimate ABL as a percentage of root length, achieving moderate reliability (ICC = 0.601), with higher accuracy for non-molars (ICC = 0.763) but reduced performance in severe ABL cases. These studies emphasize the importance of precise anatomical landmark localization in automated periodontal assessment. Among the most innovative contributions is PDCNN, introduced by [PERSON] et al. [38] which employs a two-stage CNN with anchor-free encoding for RBL diagnosis. PDCNN outperformed Faster R-CNN and YOLOv4, achieving an accuracy of 0.762 while maintaining real-time processing speeds (39.4 FPS). The model's efficiency highlights the potential of optimizing detection algorithms for clinical applications, reducing the computational burden without sacrificing diagnostic accuracy. Similarly, [PERSON] et al. [39] integrated YOLOv5, VGG-16, and U-Net to develop a model capable of segmenting and measuring PBL across 8000 images. With an accuracy of 97.0% for RBL detection, the model surpassed human diagnostic performance but required further refinement to ensure robustness across diverse clinical settings. Further extending DL applications, [PERSON] et al. [40] utilized a U-Net model to detect horizontal and vertical bone loss, achieving strong performance for total (AUC = 0.951) and horizontal loss (AUC = 0.910) but lower for vertical loss (AUC = 0.733). The disparity in detection performance for different bone loss types underscores the challenge of developing models that generalize well across all clinical scenarios. In another study, [PERSON] et al. [41] combined U-Net and Mask-RCNN and enabled precise segmentation and keypoint localization for bone loss ratio measurement. Diagnostic accuracy was highest for stage III (97%) and lower for stages I and II. The model reached sensitivity, specificity, and F-measure values of 0.84, 0.88, and 0.81, respectively, suggesting it can effectively support periodontal diagnosis and reduce clinician workload. Similarly, [PERSON] et al. [42] employed Faster R-CNN for detecting periodontal compromised teeth in panoramic radiographs, achieving a sensitivity of 0.84 and specificity of 0.88, highlighting its potential in clinical decision support. However, as with many DL models, the study noted challenges in interpretability, a critical barrier to widespread clinical adoption. Another noteworthy study by [PERSON] et al. [43] compared various DL models for classifying periodontal disease stages, identifying DenseNet121 with global average pooling (GAP) and the minimum redundancy maximum relevance (mRMR) feature selection method as the most effective combination. Achieving a classification accuracy of 90.7%, this approach demonstrated the potential of feature selection techniques in optimizing model performance while reducing computational complexity. Lastly, [PERSON] et al. [44] applied Mask R-CNN and YOLOv8 for periodontal disease analysis, achieving 97.01% accuracy in tooth detection and high segmentation performance for teeth, bones, and crowns. Their study emphasized the importance of preprocessing techniques, such as CLAHE image enhancement, in improving model precision and reducing diagnostic time.