AI-Driven Dental Radiography Analysis: Enhancing Diagnosis and Education Through YOLOv8 and Eigen-CAM

Abstract

This study is an artificial intelligence (AI)-supported system that aims to help dentists and students by analyzing dental X-rays and detecting certain diseases in teeth. This system aims to help students in the learning process by quickly detecting procedures such as dentin decay, root canal treatment, implants, crowns, fillings in dental X-rays. The datasets obtained through Roboflow were subjected to labeling process. The dataset consists of approximately 2500 dental X-ray images containing dental diseases and procedures performed on teeth, consisting of 5 different classes. The classes identified in these images were labeled. After this labeling process, a deep learning model was developed using YOLOv8 architecture. Eigen-CAM was added to the model and its performance was tested. Eigen-CAM helped to finalize the results of the model by visualizing them. After all these processes, the model was integrated into a web interface and made available for use. The results of this study show that the proposed method is very fast and effective in analyzing dental X-rays. The results of the study have made significant contributions to dentists and dental students in terms of early diagnosis and learning process and have the potential to positively affect clinical decision support processes.