In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

Maxillofacial osteosynthetic surgeries require stable fixation for uneventful boney healing and optimal remodeling. Although conventional titanium plates and screws for osteofixation are considered the gold standard for rigid fixation in maxillofacial surgeries, bioresorbable implants of plates and screw systems are commonly used for various maxillofacial osteosynthetic surgeries such as orthognathic surgery, maxillofacial fractures, and reconstructive surgery. Titanium plates are limited by their palpability, mutagenic effects, and interference with imaging, which may lead to the need for subsequent removal; the use of a biologically resorbable osteofixation system could potentially address these limitations. However, several problems remain including fundamental issues involving decreased mechanical strength and stability, slow biodegradation, complex procedures, and the available bioresorbable implant materials. Major advances in bioresorbable plate systems have been made with the use of bioactive/resorbable osteoconductive materials and an accelerator of bioresorption, such as polyglycolic acid. This report presents an overview of currently available resorbable implant materials and their applications, with a focus on recent innovative advances and new developments in this field.

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.

The oral and maxillofacial region has a complicated anatomy with critical contiguous organs, including the brain, eyes, vital teeth, and complex networks of nerves and blood vessels. Therefore, advances in basic scientific research within the field of intraoperative oral and maxillofacial surgery have enabled the introduction of the features of these techniques into routine clinical practice to ensure safe and reliable surgery. A navigation system provides a useful guide for safer and more accurate complex in oral and maxillofacial surgery. The effectiveness of a navigation system for oral and maxillofacial surgery has been indicated by clinical applications in maxillofacial trauma surgery including complex midfacial fractures and orbital trauma reconstruction, foreign body removal, complex dentoalveolar surgery, skull base surgery including surgery of the temporomandibular joint (TMJ), and orthognathic surgery. However, some fundamental issues remain involving the mobility of the mandible and difficulty in updating images intraoperatively. This report presents an overview and feasible applications of available navigation systems with a focus on the clinical feasibility of the application of navigation systems in the field of oral and maxillofacial surgery and solutions to current problems.

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.