Dental Teeth X-Ray Image Classification Using AI

The use of artificial intelligence (AI) and machine learning (ML) in healthcare has seen significant growth in recent years. In this study, we explored the potential of deep learning techniques for dental teeth detection and the identification of teeth as “normal”, “implant”, “root”, “erupting”, and “missing” using X-ray images. Traditionally, dentists rely on visual-tactile methods to diagnose oral conditions. However, these methods have limitations, such as inefficiency in time spent on diagnosis, the high cost of diagnosis, and subjectivity in the diagnosis. To address these limitations, we developed an automated algorithm that recognizes teeth structures using computer vision technology and ML methods. Our algorithm was trained on a dataset of 340 adult teeth X-ray radiographs and optimized through a series of experiments to determine the best training hyper-parameters. The development of an AI-based clinical decision support system for dental diagnosis can increase efficiency and accuracy in clinical decision-making. Our study contributes to the field of dentistry by exploring the potential of AI and ML techniques for teeth recognition and detection. Additionally, we provided mathematical explanations of our observations to aid in the interpretation of our results. Our optimized algorithm achieved a good precision of 78.2%. Overall, our study successfully demonstrated the potential of AI and ML in dental healthcare, specifically in teeth detection and implant identification using X-ray images.