An Approach to Detection of Dental Restorations on Panoramic Radiographs

Dental health is vital to our overall health. Teeth damaged by decay or trauma are often repaired with dental restorations such as fillings, crowns and bridges. Panoramic radiography is a vital tool in dentistry as it allows visualization of teeth, jaws and surrounding tissues. Analyzing these images is time-consuming and prone to human error. Deep learning and computer vision have made it possible to use automated systems for dental diagnosis. This study presents an artificial intelligence-based approach to detect anomalies in dental x-ray images and make related diagnoses. The YOLOv8 model was trained and tested on a set of data taken from the DENTEX dataset. The recommended model is 94% in the filler class, 98% in the root canal treatment class, and 99% in the crown class. It was able to classify with an accuracy value of 99% in the bridge class and 94% in the implant class. The results show that the proposed method is a potentially valuable tool in dental radiology.