Background: Maxillary sinus augmentation (MSA) is a standard and predictable procedure to increase bone height in the atrophic posterior maxilla. Many biomaterials are employed in this technique; however, autologous platelet concentrates have been found to reduce clinical recovery time and improve bone gain in MSA.
Purpose: This study aimed to compare the radiographic, histomorphometric, and implant stability outcomes of titanium-prepared platelet-rich fibrin (T-PRF) and deproteinized bovine bone mineral (DBBM) in a two-stage MSA technique.
Purpose: This study aimed to evaluate the relationship between risk profile assessments of dental implants that have been in function for at least two-year and peri-implant marginal bone loss during the follow-up period using the Implant Disease Risk Assessment Diagram. Material and Methods: A total of 70 patients and 170 implants who had been functionally loaded for at least two years and who attended follow-up sessions were included in the study. Full-mouth plaque index (PI), gingival index (GI), probing depth (PD), bleeding on probing, clinical attachment level (CAL), and peri-implant modified plaque index, modified bleeding index, PD, keratinized mucosal width (KMW), CAL and GR were recorded.
View Article and Find Full Text PDFObjectives: The presence of insufficient peri-implant supracrestal tissue height (STH) may increase marginal bone resorption. This study aims to evaluate the effect of STH on marginal bone level changes (ΔMBC) in platform-switching posterior implants placed crestally and subcrestally.
Methods: A total of 80 implants were included in this study.
Purpose: To evaluate the clinical and radiographic results of simultaneous implant placement using transcrestal sinus floor elevation (TSFE) with and without enamel matrix derivative (EMD) application.
Materials And Methods: Twenty-four patients were randomly assigned into two groups: The EMD+TSFE group (n = 13 patients, 20 implants) received TSFE with EMD application, and the TSFE group (n = 11 patients, 20 implants) received TSFE without EMD application. The patients were recalled at 3 (T3) and 12 (T12) months postsurgery.
Objectives: The objective of this study is to assess the accuracy of computer-assisted periodontal classification bone loss staging using deep learning (DL) methods on panoramic radiographs and to compare the performance of various models and layers.
Methods: Panoramic radiographs were diagnosed and classified into 3 groups, namely "healthy," "Stage1/2," and "Stage3/4," and stored in separate folders. The feature extraction stage involved transferring and retraining the feature extraction layers and weights from 3 models, namely ResNet50, DenseNet121, and InceptionV3, which were proposed for classifying the ImageNet dataset, to 3 DL models designed for classifying periodontal bone loss.