# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 4 Discussion  
### Critical Analysis and Comparison with Related Study  
Recent literature has demonstrated a growing interest in leveraging AI, particularly CNN-based deep learning models, to address the diagnostic limitations of traditional methods in detecting PBL. Our review of over 25 studies revealed a variety of deep learning architectures, datasets, and diagnostic goals applied to different types of dental radiographs, including bitewing, periapical, and panoramic images. Several notable studies have demon  
Figure 11: (**a**) Panoramic radiograph sample with multiple areas classified as mild periodontitis stage, (**b**) sample with multiple areas classified as moderate periodontitis stage, and (**c**) sample with multiple areas classified with all periodontitis stages. Figure 10: Recall-confidence curve for our YOLOv8 model. strated the potential of CNN-based models in detecting periodontal conditions across these modalities [1; 4; 5; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. For instance, [PERSON] et al. [5] used YOLOv4 for bitewing radiographs, achieving high accuracy in detecting caries and periodontitis. [PERSON] [4] utilized deep CNNs for periapical images, reporting 72.8% accuracy in assessing periodontitis severity. [PERSON] et al. [9] implemented Mask R-CNN on panoramic radiographs, demonstrating expert-level segmentation performance, while [PERSON] et al. [1] achieved an AUC of 0.91 using Faster R-CNN on a large-scale dataset. Additionally, [PERSON] et al. [17] explored CNNs for bone loss classification in periapical images, and [PERSON] et al. [12] incorporated image enhancement techniques to improve CNN-based performance. Other efforts, such as those by [PERSON] et al. [16] and [PERSON] et al. [11], focused on optimizing model architectures and preprocessing pipelines to enhance diagnostic reliability. Despite these promising developments, many of these studies primarily focused on detection rather than granular severity classification. Moreover, their performance and generalizability were often constrained by the use of limited, imbalanced, or modality-specific datasets. In response to these gaps, we developed and evaluated two complementary AI models, MobileNetV2 and YOLOv8, for the detection and classification of PBL from panoramic radiographs, aiming to establish an efficient two-stage screening system. MobileNetV2 achieved a high binary classification accuracy of 0.88, making it suitable for distinguishing between healthy and diseased cases. YOLOv8, in contrast, was tailored for detailed severity classification, reaching a significant precision of 0.74. Together, these models form a cohesive diagnostic framework that not only automates detection but also delivers clinically meaningful insights to support dentists in decision-making. The thorough method of diagnostic categorization is illustrated and exemplified in Figure 11, highlighting the models' effectiveness in identifying and classifying PBL at various severity levels. Furthermore, all results were validated by expert dental practitioners to ensure clinical applicability. Compared to previous studies, our proposed system introduces several key advancements that directly address the identified research gaps. Most notably, it is among the first AI-based systems designed specifically for classifying different severity levels of PBL using panoramic radiographs, a need strongly emphasized by dental experts. This severity classification supports a more nuanced and efficient diagnostic process, reducing clinicians' workload and enabling timely treatment decisions. Our novel two-stage framework, using MobileNetV2 for binary detection and YOLOv8 for severity classification, offers a comprehensive and clinically relevant solution. By exclusively leveraging panoramic radiographs, which are widely used for their broad coverage and low radiation exposure, our system aligns with common diagnostic practices.