Automated Detection of Dental Conditions: Utilizing Machine Learning for Improved Diagnostic Precision and Standardization

Abstract

This research introduces a custom convolutional neural network (CNN) model aimed at automating the detection and classification of dental conditions using panoramic X-ray images. By prioritizing improvements in diagnostic accuracy and operational efficiency, the model achieves an accuracy of 90.2% with a low data loss of 0.15. A diverse dataset comprising various dental conditions—including cavities, periodontal disease, dental fractures, and healthy teeth—was carefully assembled and pre-processed to provide high-quality input for training. Key performance metrics such as precision, recall, and F1-score were analysed, highlighting the model's effective capabilities in diagnosing dental issues. The proposed model outperforms traditional CNNs and other transfer learning techniques, although it also points to potential enhancements, especially in recognizing dental fractures. Future work will focus on further refinements and the evaluation of real-world patient outcomes, emphasizing the significant role of machine learning in advancing dental diagnostics and enhancing patient care.