Detection and Numbering of Teeth in Panoramic Radiographic Images Using Deep Neural Networks

Abstract

oday, with the advancement of the artificial intelligence methods, it is possible to automatically evaluate these images in order to save the clinician's time. Purpose. To employ Convolutional Neural Networks (CNNs) for tooth segmentation and numbering in panoramic radiographic images. The study utilized a dataset with ample volume and diversity, and employed cutting-edge deep learning algorithms for the task of tooth segmentation and numbering. Implementing and utilizing this method can enhance the efficiency of clinical diagnosis and treatment procedures.Materials and Methods. The data set includes 527 panoramic images that were selected from the archives of the Radiology Department of the Faculty of Dentistry of Tabriz. After that, the images were labeled by an oral and maxillofacial radiologist, according to the FDI numbering system. The segmentation was done by using the U-Net architecture and its output entered the VGG-16 network for numbering. Eighty percent of the data was used for network training and 10% for validation and another 10% for network testing.Results. The results obtained from the U-Net network for tooth segmentation, based on the original data; sensitivity, specificity, and Dice, are 98.9%, 98.4%, and 95.4%, respectively. Also, for teeth numbering by using the VGG-16 Network Architecture, we obtained sensitivity, specificity and accuracy equal to 98.58%, 99.93% and 96.8%, respectively. In the examination and diagnosis of the implant, the retained roots, and the extracted teeth, the accuracy of 98.45%, 97.1%, and 98.2% was obtained, respectively.Conclusion. The obtained results are favorable compared to similar studies, and in the future, with the development of these methods, it can be a useful help in the automatic analysis of panoramic images and other dental images.