## 2 Materials and Methods
### Convolutional Neural Network (CNN)  
Several studies have explored CNN-based approaches for detecting and quantifying PBL in panoramic and periapical radiographs. [PERSON] et al. [29] developed a seven-layer CNN trained on 2001 image segments, achieving an accuracy of 0.81, outperforming human clinicians in specificity while slightly lagging in sensitivity. The study highlighted the potential of AI to standardize periodontal diagnostics, reducing inter-examiner variability. However, the model's performance discrepancies between different tooth types underscored the need for further refinement, particularly for complex cases involving incisors. In comparison, [PERSON] et al. [30] introduced DeNTNet, a multi-phase DL model incorporating transfer learning and region-of-interest (ROI) segmentation to enhance lesion detection and tooth classification. DeNTNet outperformed human clinicians with an F1 score of 0.75 versus 0.69, demonstrating that structured training approaches significantly impact model efficacy. The inclusion of an ablation study further reinforced the importance of each model component, particularly segmentation and knowledge-driven transfer learning, in optimizing diagnostic precision. Beyond basic CNN models, advanced architectures such as VGG-19, Mask R-CNN, and YOLO have been explored for periodontal assessment. [PERSON] et al. [31] employed a VGG-19-based CNN to predict the extraction probability of periodontally compromised teeth (PCT), achieving an accuracy of 81% for premolars and 76.7% for molars. The study revealed that tooth morphology influences diagnostic performance, with premolars exhibiting better predictive accuracy due to their simpler anatomical structure. Meanwhile, [PERSON] et al. [32] introduced Deetal-Perio, a Mask R-CNN-based system integrating segmentation and alveolar bone loss (ABL) calculation, achieving F1 scores of 0.894 and 0.820 across two datasets. The study demonstrated that hybrid approaches combining segmentation and machine learning classifiers, such as XGBoost, could improve interpretability and diagnostic consistency. Similarly, YOLO-based models have gained traction for their real-time detection capabilities. [PERSON] et al. [33] applied YOLOv5 to detect ABL in panoramic radiographs, emphasizing its effectiveness in localized segmentation, particularly for maxillary incisors. The study highlighted the need for larger datasets and 3D imaging integration to further enhance precision. [PERSON] et al. [34] extended CNN applications by using VGG-16 for classifying ABL severity in periapical radiographs, achieving 73% accuracy in binary classification but struggling with multi-class staging. This limitation suggests that while CNNs effectively detect PBL, they require additional enhancements to accurately determine disease severity. Another avenue of exploration involves super-resolution techniques for improving radiographic image quality. [PERSON] et al. [35] assessed the use of SRCNN and SRGAN for enhancing resolution in periodontal radiographs, demonstrating improved contrast and edge definition. However, while these enhancements benefited visual assessment, they introduced inconsistencies in classification accuracy, with artifacts sometimes disrupting  
Figure 5: Hybrid neural network structure. CNN performance. This finding underscores the necessity for balanced pre-processing techniques that enhance image quality without compromising diagnostic integrity. In terms of automated measurement techniques, landmark-based approaches have been explored for PBL quantification. [PERSON] et al. [36] developed an automated system for localizing the cementoenamel junction (CEJ), alveolar crest (ALC), and apex (APEX), achieving 90% localization accuracy within \(\sim\)0.44 mm deviation. More than half of the system's ABL measurements deviated less than 10% from the ground truth, showcasing its potential for early diagnosis and treatment monitoring. Similarly, [PERSON] et al.