Abstract
Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.
Highlights
Modern dentistry employs computer-assisted procedures in common dental treatments such as surgical planning, postoperative assessment, mechanized dental implants, and orthodontic planning [1].The numbering of teeth in dental radiology is a routine evaluation that takes up time
This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs
The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions
Summary
Modern dentistry employs computer-assisted procedures in common dental treatments such as surgical planning, postoperative assessment, mechanized dental implants, and orthodontic planning [1]. The numbering of teeth in dental radiology is a routine evaluation that takes up time. Dental images have been used combined with artificial intelligence in many applications such as dental diagnosis and dental treatment [2, 3]. For example, to identify human dental images, in routine dental procedures, maxillofacial surgical applications, and teeth generic modelling [4]. Neural networks used for image recognition have evolved over time: initially started using Regions with Convolutional Neural Networks (R-CNNs) for classification tasks and continued with the use of fast R-CNN for classification and detection [6, 7]
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