This study aimed to compare the diagnostic accuracy of cone-beam computed tomography (CBCT) unit with digital intraoral radiography technique for detecting periodontal defects. The study material comprised 12 dry skulls with maxilla and mandible. Artificial defects (dehiscence, tunnel, and fenestration) were created on anterior, premolar and molar teeth separately using burs. In total 14 dehiscences, 13 fenestrations, eight tunnel and 16 without periodontal defect were used in the study. These were randomly created on dry skulls. Each teeth with and without defects were images at various vertical angles using each of the following modalities: a Planmeca Promax Cone Beam CT and a Digora photostimulable phosphor plates. Specificity and sensitivity for assessing periodontal defects by each radiographic technique were calculated. Chi-square statistics were used to evaluate differences between modalities. Kappa statistics assessed the agreement between observers. Results were considered significant at P < 0.05. The kappa values for inter-observer agreement between observers ranged between 0.78 and 0.96 for the CBCT, and 0.43 and 0.72 of intraoral images. The Kappa values for detecting defects on anterior teeth was the least, following premolar and molar teeth both CBCT and intraoral imaging. CBCT has the highest sensitivity and diagnostic accuracy for detecting various periodontal defects among the radiographic modalities examined.

This study assessed the influence of different voxel resolutions of two different CBCT units on the in vitro detection of periodontal defects. The study used 12 dry skulls with a maxilla and a mandible. Artificial defects (dehiscence, tunnel, fenestration) were separately created on the anterior, premolar and molar teeth using burrs. A total of 14 dehiscences, 13 fenestrations, 8 tunnels and 16 non-defect controls were used in the study. Images were obtained from two different CBCT units in six voxel sizes (voxel size: 0.080, 0.100, 0.125, 0.150, 0.160 and 0.200 mm3). Kappa coefficients were calculated to assess both intra- and interobserver agreements for each image set. Overall intraobserver kappa coefficients ranged between 0.978 and 0.973 for the 0.080-mm3 images and between 0.751 and 0.737 for the 0.160-mm3 images, suggesting notably high intraobserver agreement for detecting periodontal defects. CBCT performed significantly better at detecting fenestrations (p0.05). A voxel size of 0.150 mm3 was identified as the cut-off point for overall detection of periodontal defects. CBCT should be considered the most reliable imaging modality for the diagnosis of periodontal defects.

Background and Objectives: We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence. Materials and Methods: The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis. Results: The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes. Conclusions: This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction.

Implant failure may necessitate another surgical procedure to improve implant survival. Photofunctionalization(PF) with UV light is a promising method to enhance dental implant osseointegration. The study evaluates the effect of the PF on bone quality around the implants using Fractal Dimension Analysis (FDA). A total of 42 implants were placed in a split-mouth design and evaluated using FDA on panoramic radiographs taken pre- and post-operatively. Regions of interest (25 × 25 pixels) were analyzed using ImageJ. Statistical analysis used SPSS 26 with a 95% confidence level. Shapiro-Wilk test confirmed non-normal distribution. A statistically significant increase in Fractal dimension(FD) values was observed in the test group between pre- and post-operative images (p = 0.044). The control group showed a non-significant increase (p = 0.794). PF appears to enhance osseointegration by improving bone microstructure as shown by increased FD values. FDA is a reliable imaging-based proxy for evaluating implant-related bone changes. Current Controlled Trials NCT06821932 (Retrospectively registered) ( https://clinicaltrials.gov/study/NCT06821932 /2025-02-11).

Aim: Bruxism is defined as the involuntary recurrent masticatory muscle activity characterized by gnashing, grinding, clenching of teeth, and/or pushing the mandible. Factors creating its etiology are peripheral (morphological) or central (physiopathological and physiological), and exogenous. Recently, among physiological factors, depression and bruxism were considered to be related. A definitive treatment method does not exist for bruxism; however, botulinum toxin-A (BT-A) application is an up-to-date and effective way of treatment. The present study is aimed to evaluate the levels of depression in bruxism patients treated with BT-A application. Materials and Methods: A total of 25 individuals (23 females and 2 males) who were diagnosed as bruxism patients were included in the study. 25 U of BT-A for each masseter muscle was injected into the patients. Patients were prospectively observed for a possible change in depression levels by using Beck’s Depression Inventory. The inventory was implemented before and six months after the BT-A application. Depression levels before and six months after the injection were compared. A paired t -test was used to compare “before” and “after” treatment values. One-way analysis of variance and post-hoc Tukey tests were used to evaluate the change in Beck’s Depression Inventory scores according to age groups. Results: The mean total score was 7.80 ± 8.10 before the treatment and 7.16 ± 6.52 six months after the treatment. The decrease in the mean score was not statistically significant ( P > .05). Conclusion: In conclusion, despite the decrease in the mean Beck’s Depression Inventory scores, a statistically significant decrease in the depression levels of patients was not observed.