## 2 Materials and Methods
### Hybrid Neural Network  
Four studies used hybrid neural networks instead. We reported on the [PERSON] et al. [45] study, which developed a hybrid DL method to diagnose periodontal bone loss and stage periodontitis using panoramic radiographs (Figure 6). DL was applied to detect radiographic bone and CEJ levels for the entire jaw, while percentage-based analysis of bone loss relative to tooth long axes enabled classification of periodontal bone loss. This classification aligns with the 2017 World Workshop periodontitis staging criteria. The method achieved a Pearson's correlation coefficient of 0.73 and an intraclass correlation value of 0.91 (\(p<0.01\)) when compared with radiologist diagnoses, demonstrating high accuracy and reliability. This hybrid framework shows promise for automatic, reliable periodontitis diagnosis and staging. A CNN identified bone and CEJ levels, achieving 0.92-pixel accuracy and strong correlation with expert diagnoses. While effective overall, incisor and molar staging was less accurate due to complexity. The method shows promise for consistent diagnostics, with further research needed on larger datasets and complex cases for broader clinical applications.  
Figure 6: Complete process for detecting and categorizing periodontal bone loss using a hybrid framework that combines the traditional CAD technique with DL architecture. Reprinted from Ref. [45]. Copyright 2021, Scientific Reports. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license.  
Instead, [PERSON] et al. [46] presented in their study a DL model to diagnose periodontal bone loss and stage periodontitis in dental panoramic radiographs. The study developed a CNN-based ensemble, using YOLOv8, Mask R-CNN, and TransUNet models for tooth detection, segmentation, and bone loss measurement. It was tested on 320 radiographs with 8462 teeth from January 2020 to December 2023. The DL method demonstrated a periodontal bone loss degree deviation of 5.28%, a Pearson's correlation coefficient of 0.832 (\(p<0.001\)), and an intraclass correlation value of 0.806 (\(p<0.001\)). The model showed an 89.45% diagnostic accuracy, closely aligning with professional diagnoses. The method holds promise as a reliable aid for dentists, especially in resource-limited settings, and supports consistent periodontal assessments.  
[PERSON] et al. [47] developed a machine learning-based decision system for staging and grading periodontitis using clinical and radiographic data. Algorithms like decision trees, random forests, and SVM were tested, achieving up to 98.6% accuracy. For image-only staging, a hybrid model of ResNet50 and SVM reached 88.2% accuracy. Despite lower accuracy in grading from images, the model offers a promising tool for aiding clinicians, especially for complex diagnoses, with plans for further optimization. Finally, this study by [PERSON] et al. [48] introduced an unsupervised few-shot learning model using UNet architecture and convolutional variational autoencoder (CVAE) for periodontal disease diagnosis in dental radiographs, addressing limited labeled data issues. The validation, comparing this method with traditional supervised learning and autoencoder-based clustering, shows a significant improvement in diagnostic accuracy and efficiency. For three real-world validation datasets, the Unet-CVAE model achieved 14% higher accuracy than supervised models with 100 labeled images across datasets. UNet extracts regions of interest, while CVAE clusters features, improving diagnostic accuracy and data efficiency in dental imaging. This framework is promising for data-scarce clinical applications.