Innovative Diagnosis of Dental Diseases Using YOLO V8 Deep Learning Model

Abstract

The diagnosis and identification of dental problems pose significant challenges. Traditionally, dental disease diagnosis was a manual and time-consuming process, requiring dentists to meticulously examine and evaluate the condition. The integration of artificial intelligence (AI) represents a transformative approach to aid in medical imaging diagnostics. Specifically, leveraging AI for diagnosing dental issues entails the automatic localization of lesions. In this study, the Yolo V8 deep learning model is employed to develop an innovative method for the detection and categorization of common dental problems. The primary objective of this approach is to establish a comprehensive database comprising two distinct categories of dental X-ray images: BiteWing X-ray Images and Orthopantomography X-ray (OPG). These categories aim to facilitate the diagnosis and classification of various dental diseases. The results of the experiments showed that the best performance in training YOLOv8m was achieved with mAP of 71.6%, recall of 90%, and precision of 90%.