Background/Objectives: Coated tongue is a common oral condition with notable clinical relevance, often overlooked due to its asymptomatic nature. Its presence may reflect poor oral hygiene and can serve as an early indicator of underlying systemic diseases. This study aimed to develop a robust diagnostic model utilizing convolutional neural networks and machine learning classifiers to improve the detection of coated tongue lesions. Methods: A total of 200 tongue images (100 coated and 100 healthy) were analyzed. Images were acquired using a DSLR camera (Nikon D5500 with Sigma Macro 105 mm lens, Nikon, Tokyo, Japan) under standardized daylight conditions. Following preprocessing, feature vectors were extracted using CNN architectures (VGG16, VGG19, ResNet, MobileNet, and NasNet) and classified using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) classifiers. Performance metrics included sensitivity, specificity, accuracy, and F1 score. Results: The SVM + VGG19 hybrid model achieved the best performance among all tested configurations, with a sensitivity of 82.6%, specificity of 88.23%, accuracy of 85%, and an F1 score of 86.36%. Conclusions: The SVM + VGG19 model demonstrated high accuracy and reliability in diagnosing coated tongue lesions, highlighting its potential as an effective clinical decision support tool. Future research with larger datasets may further enhance model robustness and applicability in diverse populations.

In terms of new technologies that continue to have an impact on daily life, artificial intelligence (AI) covers a wide range. Due to the development of AI, it is now feasible to analyze large amounts of data, which enhances decision-making by supplying accurate information. Dentistry, which is intertwined with technology, is an area open to development with artificial intelligence applications. AI applications come to the fore in areas such as diagnosis of various pathologies, planning of complex treatments and robotic surgery. The aim of this section is to review the current and potential uses of AI applications in dentistry, to examine the innovations and possible contributions to the field.

Objective: To evaluate the performance of vision transformer (ViT)-based deep learning models in the classification of open apex on panoramic radiographs (orthopantomograms (OPGs)) and compare their diagnostic accuracy with conventional convolutional neural network (CNN) architectures. Materials and Methods: OPGs were retrospectively collected and labeled by two observers based on apex closure status. Two ViT models (Base Patch16 and Patch32) and three CNN models (ResNet50, VGG19, and EfficientNetB0) were evaluated using eight classifiers (support vector machine (SVM), random forest (RF), XGBoost, logistic regression (LR), K-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), and multi-layer perceptron (MLP)). Performance metrics (accuracy, precision, recall, F1 score, and area under the curve (AUC)) were computed. Results: ViT Base Patch16 384 with MLP achieved the highest accuracy (0.8462 ± 0.0330) and AUC (0.914 ± 0.032). Although CNN models like EfficientNetB0 + MLP performed competitively (0.8334 ± 0.0479 accuracy), ViT models demonstrated more balanced and robust performance. Conclusions: ViT models outperformed CNNs in classifying open apex, suggesting their integration into dental radiologic decision support systems. Future studies should focus on multi-center and multimodal data to improve generalizability.

Aim: This study aimed to perform clinical diagnosis and treatment planning of mucous retention cysts with high accuracy and low error using the deep learning-based EfficientNet method. For this purpose, a hybrid approach that distinguishes healthy individuals from individuals with mucous retention cysts using panoramic radiographic images was presented. Material and Methods: Radiographs of patients who applied to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Fırat University between 2020 and 2022 and had panoramic radiography for various reasons were evaluated retrospectively. A total of 161 radiographs, 82 panoramic radiographs with mucous retention cysts and 79 panoramic radiographs without mucous retention cysts, were included in the study. In the classification process, deep feature representations or feature maps of the images were created using eight different deep learning models of EfficientNet from B0 to B7. The efficient features obtained from these networks were given as input to the support vector machine classifier, and healthy individuals and patients with mucous retention cysts were classified. Results: As a result of the model training, it was determined that the EfficientNetB6 model performed the best. When all performance parameters of the model were evaluated together, the accuracy, precision, sensitivity, specificity, and F1 score values were obtained 0.878, 0.785, 0.916, 0.857, and 0.846, respectively. Conclusion: The proposed hybrid artificial intelligence model showed a successful classification performance in the diagnosis of mucous retention cysts. The study will shed light on other future studies that will serve the same purpose.