Automatic Detection and Classification of Dental Restorations in Panoramic Radiographs

Abstract

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Automatic semantic segmentation for dental restorations in panoramic radiography images using U‐Net model

“…The proposed model's performance was reported to be 93.6%. Yeshua et al 20 achieved 92% accuracy in detecting restorations in different categories on panoramic images using adaptive threshold and machine learning methods. Our proposed Unet with ResNext50 backbone segmentation model achieved a mean IoU of 76.7% and PA of 99.81%.…”

“…However, the accuracy gained has various drawbacks, including using a human to define the initial settings and an unbalanced dataset for composite fillings and dental implants. A similar study was conducted by Yeshua et al 20 to automatically map the dental restorations: fillings, crowns, root treatments, and implants in a panoramic radiograph using computer vision and machine learning techniques. A total of 316 restorations were marked based on the evaluation of 63 panoramic radiographs.…”

Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm

“…Although Chen et al in the study 28 developed an advanced image cropping technique combined in conjunction with CNN models (AlexNet 29 , GoogLeNet 30 , and SqueezeNe 31 ) to classify missing, treated, and normal teeth from cropped images, they did not incorporate teeth numbering. Other studies have solely focused on classifying disease 32 and restoration types 33 without teeth numbering. However, DENTECT 34 and the model proposed by Muresan et al 35 could detect and enumerate teeth as well as dental therapies in panoramic X-ray images.…”

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

“…Compared to other dental X-ray films, it has the important advantage of covering most anatomical structures and clinical findings in a single image [8]. This important feature facilitates analysis by PANO experts and provides important information related to clinical diagnosis and treatment [9]. In this study, deep learning will be used to classify different symptoms of teeth.…”