An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs.

OBJECTIVES: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. METHODS: The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. RESULTS: The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. CONCLUSIONS: A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.

Detection of the separated endodontic instrument on periapical radiographs using a deep learning-based convolutional neural network algorithm.

The study evaluated the diagnostic performance of an artificial intelligence system to detect separated endodontic instruments on periapical radiograph radiographs. Three hundred seven periapical radiographs were collected and divided into 222 for training and 85 for testing to be fed to the Mask R-CNN model. Periapical radiographs were assigned to the training and test set and labelled on the DentiAssist labeling platform. Labelled polygonal objects had their bounding boxes automatically generated by the DentiAssist system. Fractured instruments were classified and segmented. As a result of the proposed method, the mean average precision (mAP) metric was 98.809%, the precision value was 95.238, while the recall reached 98.765 and the f1 score 96.969%. The threshold value of 80% was chosen for the bounding boxes working with the Intersection over Union (IoU) technique. The Mask R-CNN distinguished separated endodontic instruments on periapical radiographs.

An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs.

Bitewing radiographic imaging is an excellent diagnostic tool for detecting caries and restorations that are difficult to view in the mouth, particularly at the molar surfaces. Labeling radiological images by an expert is a labor-intensive, time-consuming, and meticulous process. A deep learning-based approach has been applied in this study so that experts can perform dental analyzes successfully, quickly, and efficiently. Computer-aided applications can now detect teeth and number classes in bitewing radiographic images automatically. In the deep learning-based approach of the study, the neural network has a structure that works according to regions. A region-based automatic segmentation system that segments each tooth using masks to help to assist analysis as given to lessen the effort of experts. To acquire precision and recall on a test dataset, Intersection Over Union value is determined by comparing the model's classified and ground-truth boxes. The chosen IOU value was set to 0.9 to allocate bounding boxes to the class scores. Mask R-CNN is a method that serves as instance segmentation and predicts a pixel-to-pixel segmentation mask when applied to each Region of Interest. The tooth numbering module uses the FDI notation, which is widely used by dentists, to classify and number dental items found as a result of segmentation. According to the experimental results were reached 100% precision and 97.49% mAP value. In the tooth numbering, were obtained 94.35% precision and 91.51% as an mAP value. The performance of the Mask R-CNN method used has been proven by comparing it with other state-of-the-art methods.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.

OBJECTIVES: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

The efficacy and limitations of USI for diagnosing TMJ internal derangements.

INTRODUCTION: This study was conducted to determine the effectiveness of ultrasonographic imaging for diagnosing temporomandibular joint internal derangements. MATERIALS AND METHODS: Ultrasonographic and magnetic resonance imaging scans of temporomandibular joints were obtained bilaterally in 55 patients who had temporomandibular joint disorders and who were diagnosed with temporomandibular joint internal derangements following a clinical examination. Diagnostic accuracy of ultrasonographic imaging was assessed considering magnetic resonance imaging as the gold standard method. RESULTS: When the results of ultrasonographic imaging and magnetic resonance imaging were compared, the diagnostic accuracy of ultrasonographic imaging was 0.81 for detecting TMJ disc displacement. The diagnostic accuracy of ultrasonographic imaging in detecting TMJ disc position was 0.81 in the closed-mouth position and 0.93 in the open-mouth position. CONCLUSION: As a noninvasive and reproducible imaging method acquiring dynamic images, ultrasonographic imaging is a successful method in the evaluation of temporomandibular joint disc displacement.

Imaging of masseter muscle spasms by ultrasonography: a preliminary study.

OBJECTIVES: The aim of this study was to determine the effectiveness of ultrasonography (USG) in locating spasm points in the masseter muscle. METHODS: Fifteen patients with TMJ dysfunction and five healthy controls were included in the study. First clinical examination of TMJ and palpation of masticatory muscles were done. Then, the masseter muscles were examined by USG. A total of 40 masseter muscles were examined within the study. RESULTS: Spasm points were observed as limited isoechogenic areas within normal heterogeneous muscle tissue. Within the 30 masseter muscles of patients with TMJ dysfunction, a total of 14 spasm points were detected clinically and 18 spasm points were detected ultrasonographically. No clinic or sonographic spasm point was detected in the masseter muscles of healthy controls. CONCLUSION: USG demonstrated in detail the internal structure of the masseter muscle in all patients and provided precise localization of the spasm points on the muscle. This is a preliminary study, showing that changes in muscle internal structure can be visualized with USG.