Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.

Oral candidiasis presents with multiple clinical manifestations. Among known pathogenic Candida species, Candida albicans is the most virulent and acts as the main causative fungus of oral candidiasis. Novel treatment modalities are needed because of emergent drug resistance and frequent candidiasis recurrence. Here, we evaluated the ability of Lacticaseibacillus rhamnosus L8020, isolated from healthy and caries-free volunteers, to prevent against the onset of oral candidiasis in a mouse model. Mice were infected with C. albicans, in the presence or absence of L. rhamnosus L8020. The mice were treated with antibiotics and corticosteroid to disrupt the oral microbiota and induce immunosuppression. We demonstrated that oral consumption of L. rhamnosus L8020 by C. albicans-infected mice abolished the pseudomembranous region of the mouse tongue; it also suppressed changes in the expression levels of pattern recognition receptor and chemokine genes. Our results suggest that L. rhamnosus L8020 has protective or therapeutic potential against oral candidiasis, which supports the potential use of this probiotic strain for oral health management.

Background/Objectives: Cephalometric analysis has a pivotal role in the quantification of the craniofacial skeletal complex, facilitating the diagnosis and management of dental malocclusions and underlying skeletal discrepancies. This study aimed to develop a deep learning system that predicts a cephalometric skeletal parameter directly from lateral profile photographs, potentially serving as a preliminary resource to motivate patients towards orthodontic treatment. Methods: ANB angle values and corresponding lateral profile photographs were obtained from the medical records of 1600 subjects (1039 female and 561 male, age range 3 years 8 months to 69 years 1 month). The lateral profile photographs were randomly divided into a training dataset (1250 images) and a test dataset (350 images). Seven regression convolutional neural network (CNN) models were trained on the lateral profile photographs and measured ANB angles. The performance of the models was assessed using the coefficient of determination (R²) and mean absolute error (MAE). Results: The R² values of the seven CNN models ranged from 0.69 to 0.73, and the MAE values ranged from 1.46 to 1.53. Among the seven models, InceptionResNetV2 showed the highest success rate for predictions of ANB angle within 1° of range and the highest performance in skeletal class prediction, with macro-averaged accuracy, precision, recall, and F1 scores of 73.1%, 78.5%, 71.1%, and 73.0%, respectively. Conclusions: The proposed deep CNN models demonstrated the ability to predict a cephalometric skeletal parameter directly from lateral profile photographs, with 71% of predictions being within 2° of accuracy. This level of accuracy suggests potential clinical utility, particularly as a non-invasive preliminary screening tool. The system’s ability to provide reasonably accurate predictions without radiation exposure could be especially beneficial for initial patient assessments and may enhance efficiency in orthodontic workflows.

The purpose of this study was to construct an artificial intelligence object detection model to detect the articular disk from temporomandibular joint (TMJ) magnetic resonance (MR) images using YOLO series. The study included two experiments using datasets from different MR imaging machines. A total of 536 MR images were retrospectively examined. The performance of YOLOv5 and YOLOv8 in detecting the TMJ articular disk in both normal and displaced conditions was evaluated. The impact of image-processing techniques, such as histogram equalization (HE) and contrast-limited adaptive HE (CLAHE) on model performance, was also examined. The results showed that the YOLO series could detect the articular disk regardless of displacement, with superior performance on images of normal disk position. The results suggest the applicability of object detection models in improving the diagnosis of TMJ disorders.

Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R²). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R² of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R² of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R² of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable.

In this case report, we describe a 19-year-old man who underwent an autotransplantation of a lower third molar into the extracted region of his upper central incisors. Due to trauma, the patient’s upper right and left central incisors had been extracted. He visited our clinic and requested to perform autotransplantation of his own teeth into the upper central incisor part because he wanted to use his natural teeth. So, we decided to extract his lower right third molar and autotransplant it into the extraction part of the upper central incisors. Immediately after extraction of the lower right third molar, the tooth was autotransplanted into the upper anterior region using a 3D-printed resin replica of the donor tooth and artificial sockets of the recipient site. Then, the root canal treatment was performed, and a temporary crown was set. Next, orthodontic treatment was done to flatten the curve of Spee. After completing the orthodontic treatment, a final prosthodontic restoration was set on the autotransplanted tooth. Four years later, the autotransplanted tooth remained stable with a healthy periodontium. This case demonstrates that if a patient has a request to use their natural teeth, autotransplantation of a wisdom tooth into the anterior region can be a useful method to replace the missing teeth.