Advanced AI-Assisted Panoramic Radiograph Analysis for Periodontal Prognostication and Alveolar Bone Loss Detection

Abstract Aim & Background: Periodontal diseases are a significant public health concern, affecting over a billion people worldwide. Conventional diagnostic and prognostic methods, which rely on clinical examination and radiographic interpretation for each tooth, often lack consistency and are labor-intensive and time-consuming. This highlights the need for more reliable and efficient tools to improve these processes. This study aims to develop an innovative AI-driven model for analyzing panoramic radiographs to detect alveolar bone loss and determine periodontal prognostication. Materials and methods: A balanced dataset of 2,000 panoramic radiographs, equally representing periodontally healthy individuals and those with periodontitis, was collected. Image enhancement techniques were applied, and an AI model using YOLOv8 was developed to segment teeth, identify the cemento-enamel junction (CEJ), and assess alveolar bone levels to quantify bone loss and classify the prognosis for each tooth. Results: The teeth segmentation model achieved precision, recall, and F1 scores of 0.8, 0.9, and 0.8, respectively. The CEJ and alveolar bone level segmentation model demonstrated high performance, with scores of 0.9, 1.0, and 0.9. These results highlight the model’s capability to accurately identify critical features of periodontal disease, reducing diagnostic variability and enhancing prognostic assessments. Conclusion: This AI model sets a new benchmark for detecting alveolar bone loss and determining periodontal prognostication. The developed AI model offers a promising solution by providing quicker, less labor-intensive, and more precise alternatives to current diagnostic approaches. Clinical Significance: The model’s ability to provide consistent, objective assessments can significantly reduce the workload of dental professionals, ensuring better patient outcomes.