# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 1 Introduction  
Periodontitis, affecting over 740 million people globally, is a chronic inflammatory disease that results in bone and attachment loss, potentially leading to tooth loss. Beyond its oral health implications, it is associated with systemic conditions such as heart disease, stroke, and diabetes. Early diagnosis is critical to prevent progression to advanced stages, where tooth support structures are destroyed [1]. However, current diagnostic methods, such as soft tissue probing and radiographic imaging, pose clinical challenges due to their limited reliability and dependence on expert interpretation. Radiographic imaging,particularly panoramic, bitewing, and periapical radiographs (Figure 1), is commonly used in dental clinics to support early detection and diagnosis [2].  
Traditional methods often fall short in detecting early-stage disease or assessing severity (mild, moderate, and severe), making it essential to explore more robust diagnostic tools. Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), offers promising capabilities for improving diagnostic accuracy and efficiency in identifying serious conditions like periodontitis [1; 3]. The CNN is a type of deep learning model designed to automatically learn and identify image patterns, making them highly effective in detecting and classifying periodontal bone loss from radiographs [4; 5; 6; 7]. Compared to traditional machine learning models, CNNs such as Faster R-CNN and YOLO excel in automation, speed, and reliability [2; 3; 4; 6]. They are capable of handling large datasets, reducing clinician workload, and improving precision while potentially lowering costs and minimizing misclassification in clinical practice [8; 9; 10; 11]. These benefits position CNNs as a promising tool for advancing periodontal diagnosis and management [12; 13; 14].  
Panoramic radiographs are favored in dental practice due to their wide coverage, low radiation dose, and diagnostic versatility. While several CNN-based models have utilized panoramic images in the literature, numerous limitations and challenges persist, such as small datasets, lack of standardized labeling, and limited exploration of advanced model architectures [15; 16; 17; 18]. Additional difficulties include the presence of braces, misaligned teeth, and poor image quality due to artifacts, which complicate severity assessment [4; 5; 6]. Moreover, to the best of our knowledge, no prior studies have attempted to classify the full range of periodontitis severity from panoramic images, revealing a major gap in the literature.  
This significant research gap motivates our research study, in which we propose a novel AI-based classification system to enable accurate early detection of periodontitis severity from panoramic radiographs. This study aims to develop a two-stage deep learning framework as follows: a primary CNN model detects the presence of periodontal bone loss (PBL), and a secondary model localizes and classifies its severity. This two-stage detection and classification system aims to reduce false positives in the overall diagnosis process while optimizing each model's performance, leading to an efficient and reliable diagnostic tool targeting early detection and diagnosis of PBL, particularly for use by non-experts at primary healthcare and specialized dental clinics.  
Figure 1: Types of X-ray images: (**a**) bitewing X-ray, (**b**) periapical X-ray, (**c**) panoramic X-ray [2].