Marginal Bone Level Changes Evaluation of Long-term Functional Dental Implants with the Implant Disease Risk Assessment Diagram: A 2 to 6 Years Follow-up Retrospective Study.

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.

Effect of supracrestal tissue height on marginal bone level changes at platform-switching dental implants placed crestally and subcrestally: A randomized clinical-trial.

Objectives: 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.

Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages.

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.