Objectives: This study aimed to develop and validate a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to automatically classify maxillary sinus membrane morphologies on Cone-Beam Computed Tomography (CBCT) images, distinguishing between Normal, Flat, Polypoid, and Obstruction types. Methods: A dataset of 959 CBCT images was collected and categorized into four morphological classes: Normal, Flat, Polypoid and Obstruction. A custom hybrid model was developed, integrating a lightweight residual CNN for local feature extraction, learnable weighted feature fusion with a bidirectional feature pyramid network and a Transformer encoder for global context modeling. The performance of proposed model was compared against six different architectures, including ResNet50, MobileNetV3L and standard ViT models, using accuracy, precision, recall and F1-score metrics. Results: The proposed hybrid model achieved the highest overall accuracy of 98.44%, outperforming six strong CNN and ViT models including ResNet50 (97.92%) and ViT-B16 (86.46%) models. In class-wise analysis, the model demonstrated superior diagnostic capability, particularly for the “Obstruction” class, achieving 100% accuracy. High discrimination was also observed for “Flat” (98.21%) and “Polypoid” (98.04%) morphologies, confirming the model’s sensitivity to shape-based features. Conclusions: The proposed hybrid CNN-ViT model successfully classifies maxillary sinus membrane morphologies with high accuracy, effectively overcoming the limitations of standard ViT models on limited datasets. Detection of membrane morphology is vital for predicting surgical risks like membrane perforation and post-operative sinusitis. This model serves as a reliable clinical decision support tool, enabling clinicians to objectively assess specific risk factors before implant surgery and sinus floor elevation.

Osseointegration refers to the process where an implant firmly and functionally bonds with the bone, establishing a stable union capable of bearing loads without any relative movement between the implant and the surrounding bone tissue. Post- surgery infections that may develop in the surgical area can negatively affect osseointegration, putting the success of the implant at risk. The objective of this research was to evaluate the impact of antibiotics derived from levofloxacin and cephalexin on the osseointegration of implants in the tibias of rats. A total of 21 female Sprague Dawley rats were utilized, randomly divided into three equal groups of seven rats each. Titanium implants measuring 2.5 mm in diameter and 4 mm in length were inserted into cavities created in the corticocancellous bone of the metaphyseal region of the right tibias of all animals. In the implant control group (n=7), no additional procedures were performed throughout the two- week experimental period. Subjects in the implant levofloxacin group (n=7) received subcutaneous injections of levofloxacin at a dose of 25 mg/kg, administered three times weekly for two weeks. Similarly, rats in the implant cephalexin group (n=7) were given subcutaneous injections of cephalexin at a dosage of 20 mg/kg, three times per week for two weeks. Two weeks after the operation, the implant samples in the tibias of the rats were subjected to biomechanical analysis using a digital torque device in order to evaluate the osseointegration process. As a result, it was observed that levofloxacin and cephalexin-derived antibiotics had a negative effect on implant osseointegration in the tibias of rats.

Background/Objectives: In dental implant surgeries, automatic detection and classification of maxillary sinus membrane thickening from Cone-Beam Computed Tomography (CBCT) images used for diagnosis and treatment planning, using an artificial intelligence-based system, will both reduce specialists’ error rates and enable faster processing. Therefore, in this study, a unique model was developed to automatically detect maxillary sinus membrane thickening from CBCT images. Methods: The first step of the developed model is data preprocessing. Then, feature extraction is performed using the backbone Convolutional Neural Network (CNN). The obtained features are processed in parallel with Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and Efficient Channel Attention (ECA) attention mechanisms. In this stage, channel, spatial, and local relationships are better represented. The combined features are passed to a Multilayer Perceptron (MLP) with dropout to mitigate overlearning, and the final class estimation is performed by a softmax layer. Results: Thanks to the attention mechanisms employed in the developed hybrid model, both the model’s representational power and its classification performance have improved. The model developed to automatically detect and classify maxillary sinus membrane thickening from CBCT images achieved a test accuracy rate of 99.43%. Conclusions: The developed model was compared with pre-trained models accepted in the literature and demonstrated superior performance. The current results demonstrate that the proposed model exhibits high performance in classifying maxillary sinus membrane thickness in a single-center CBCT dataset. However, independent external validation and prospective clinical evaluation are necessary to determine its potential for clinical use.

Alveolar ridge resorption following tooth loss impedes prosthetically driven dental implant placement. Although autogenous bone blocks remain the gold stan

Background/Objectives: Early diagnosis of periodontal disease is critical to prevent tissue destruction and tooth loss. This study evaluates six modern deep learning architectures for four-class radiographic periodontal bone loss grading on panoramic radiographs. Methods: This retrospective study analyzed 1053 images. Two periodontologists independently classified each image according to the most severely affected tooth into four bone loss categories: Healthy, Mild (50%). Six convolutional, transformer, and hybrid backbones (ConvNeXt-Tiny, MaxViT-Tiny, EfficientViT-B1, FocalNet-Tiny, CAFormer-S18, and DaViT-Tiny) were trained on a 70/10/20 stratified split using an NVIDIA Tesla T4 GPU, the AdamW optimizer, Cosine Annealing, and early stopping. Performance was assessed using standard classification metrics, Matthews Correlation Coefficient, Cohen’s and Quadratic Weighted Kappa, Expected Calibration Error, Area Under the Curve (AUC), McNemar’s test, and bootstrap 95% confidence intervals. Results: FocalNet-Tiny achieved the highest numerical scores among the tested models, reaching 83.41% accuracy, 84.70% balanced accuracy, 83.50% macro-F1, and 0.9610 OvR AUC. The p-values obtained from the comparisons of the models were all above 0.05, indicating that the classification performances of the evaluated models are quite similar. Bootstrap analysis confirmed stable prediction metrics for the top-scoring model. Conclusions: This study provides a controlled benchmark of modern deep learning backbones for four-class radiographic periodontal bone loss grading. FocalNet-Tiny achieved the highest numerical metrics and stable bootstrap estimates, yet performed comparably to other contemporary architectures such as ConvNeXt-Tiny. Direct radiographic classification shows promise for automated radiographic pre-screening; however, multi-center external validation and integration with clinical periodontal parameters are required before clinical implementation.