Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

OBJECTIVES: The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. METHODS: We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. RESULTS: The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). CONCLUSIONS: The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.
Bite Force* ; Bruxism* ; Dataset ; Decision Making ; Decision Support Systems, Clinical* ; Decision Support Techniques ; Humans ; Logistic Models ; Occlusal Splints ; Splints

OBJECTIVES: Ameloblastomas and keratocystic odontogenic tumors (KCOTs) are important odontogenic tumors of the jaw. While their radiological findings are similar, the behaviors of these two types of tumors are different. Precise preoperative diagnosis of these tumors can help oral and maxillofacial surgeons plan appropriate treatment. In this study, we created a convolutional neural network (CNN) for the detection of ameloblastomas and KCOTs. METHODS: Five hundred digital panoramic images of ameloblastomas and KCOTs were retrospectively collected from a hospital information system, whose patient information could not be identified, and preprocessed by inverse logarithm and histogram equalization. To overcome the imbalance of data entry, we focused our study on 2 tumors with equal distributions of input data. We implemented a transfer learning strategy to overcome the problem of limited patient data. Transfer learning used a 16-layer CNN (VGG-16) of the large sample dataset and was refined with our secondary training dataset comprising 400 images. A separate test dataset comprising 100 images was evaluated to compare the performance of CNN with diagnosis results produced by oral and maxillofacial specialists. RESULTS: The sensitivity, specificity, accuracy, and diagnostic time were 81.8%, 83.3%, 83.0%, and 38 seconds, respectively, for the CNN. These values for the oral and maxillofacial specialist were 81.1%, 83.2%, 82.9%, and 23.1 minutes, respectively. CONCLUSIONS: Ameloblastomas and KCOTs could be detected based on digital panoramic radiographic images using CNN with accuracy comparable to that of manual diagnosis by oral maxillofacial specialists. These results demonstrate that CNN may aid in screening for ameloblastomas and KCOTs in a substantially shorter time.
Ameloblastoma ; Artificial Intelligence ; Dataset ; Diagnosis* ; Hospital Information Systems ; Humans ; Jaw* ; Learning ; Mass Screening ; Odontogenic Cysts ; Odontogenic Tumors ; Oral and Maxillofacial Surgeons ; Radiography, Panoramic ; Retrospective Studies ; Sensitivity and Specificity ; Specialization

Objectives: The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeutic plans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only the patient’s oral health status obtained from an oral examination, but also other related factors such as underlying diseases, socioeconomic characteristics, and functional dependency. Methods: A Bayesian network (BN) was used as a framework to construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The faster R-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BN structure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a university hospital between January 2020 and June 2021. Results: The model showed an F1-score of 89.31%, precision of 86.69%, and recall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysis showed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). The model performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed on the recommended therapeutic plans (kappa value = 0.905). Conclusions This research was the first phase of the development of a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expected to provide general practitioners with expert-level decision support in geriatric dental care.