Advanced Deep Learning-Based Approach for Tooth Detection, and Dental Cavity and Restoration Segmentation in X-Ray Images

In recent years, much research has been focused on dental problem classification, detection, and segmentation using Deep Convolutional Neural Networks (CNN or DCNN) models. Early detection of oral issues is crucial to maintain oral health and prevent the need for invasive treatments. Dental radiographs assist in this by providing detailed images. Integrating intelligent methods with conventional imaging techniques can result in more accurate diagnoses and treatment plans for patients, dental professionals, and service providers. Our study presents a novel unified method that utilizes CNNs to segment dental restorations and cavities in X-ray images. Various datasets, including publicly available one annotated in this study, were used to evaluate the method’s performance. The first step in our method is to detect teeth, identifying each tooth’s number and location in accordance with the Universal Teeth Numbering (UTN) system. This study has also presented a new post-processing technique to address teeth ordering predicting issues in X-ray images. The detection model achieved high precision and recall metrics of over 90% after post-processing, outperforming existing methods. The second step aims to crop images of each tooth from the initial network and precisely segment the cavity and restoration. The segmentation model achieved high precision and recall of over 60%. We proportionately projected the predicted outcomes onto the original image for the final evaluation. The overall performance of our proposed method exceeded that of state-of the-art models. This method detects abnormalities that may go unnoticed by humans, reducing workload and minimizing bias in treatment assessment.