Enhancing Periodontal Bone Loss Diagnosis Through Advanced AI Techniques

Abstract

Periodontitis is a severe infection that damages the bone surrounding teeth, making severity assessment challenging for dentists. Utilizing artificial intelligence (AI) techniques enhances diagnostic accuracy in radiographic analysis, enabling a more efficient and precise diagnosis. The aim of this research is to develop an AI-based diagnostic system comprising two deep learning stages, designed to efficiently detect and classify periodontitis from panoramic radiographs. In the proposed two-stage system, a binary classifier is first utilized to determine the presence of periodontal bone loss, which is then localized and classified by the second AI model. Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. To ensure the accuracy and reliability of results, we developed two robust CNN models, YOLOv8 and MobileNet-v2, that have shown significant performance in similar applications. The primary model, MobileNet-v2, was utilized to detect periodontitis in panoramic radiographs, while the secondary model, YOLOv8, was developed to localize the affected regions and classify the severity level of the periodontitis. A custom dataset was created to comprise 817 panoramic images. Our developed YOLOv8 model achieved a testing precision of 0.74 and a recall of 0.7, while the MobileNet-v2 model achieved a testing accuracy of 0.88 and a recall of 1.0. These results were further validated by an oral radiology specialist to ensure their accuracy and reliability. Our proposed system is considered to be efficient, cost-effective, and easy to use compared to existing systems in the literature and similar software services available in the market for enhancing diagnosis of periodontitis in clinical practice.

Topics & Concepts