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Objectives: Fractal analysis is a numerical method that indicates the structural patterns and complexity of the trabecular bone on radiographs. The aim of this cross-sectional study was to evaluate the trabecular bone structure in systemically healthy patients and diabetes mellitus patients with periodontitis using fractal analysis. Method and materials: The study included 125 mandibular first molars of nonsmoker patients. The subjects were divided into five subgroups: diabetes mellitus patients with mild-moderate periodontitis, diabetes mellitus patients with advanced periodontitis, systemically healthy individuals with mild-moderate periodontitis, systemically healthy individuals with advanced periodontitis, and systemically healthy individuals with gingivitis (control group). Clinical periodontal parameters (pocket depth, bleeding on probing, clinical attachment loss, and bone loss) were recorded. Two specific sites located in the mesial-distal regions (n = 250) of the mandibular first molars were identified using periapical radiographs captured with a parallel technique. Fractal analysis values were calculated using the box-counting method. One-way analysis of variance (ANOVA), and Pearson correlation analysis were used forstatistical evaluation. Results: The highest fractal analysis values were observed in systemically healthy with gingivitis patients (mesial fractal analysis: 1.86 ± 0.01; distal fractal analysis: 1.85 ±0.01). Patients with periodontitis (mesial fractal analysis: 1.78 ±0.02; distal fractal analysis: 1.79 ± 0.01) exhibited lower fractal analysis values compared to the control group. There was no significant difference in mesial and distal fractal analysis values between all periodontitis groups. No correlation was found between age, sex, clinical attachment loss, bone loss, and fractal analysis (P>.05). Conclusions: Although fractal analysis values were lower in the periodontitis groups compared to the control group, fractal analysis did not demonstrate any periodontitis-associated changes of bone tra-beculationin diabetes at any stage of periodontitis. Furthermore, there was no significant association between fractal analysis values and age, sex, clinical attachment, and bone loss. [ABSTRACT FROM AUTHOR]

Background: The aim of this investigation was to evaluate the length, thickness, sagittal and transverse angulations and the morphological variations of the stylohyoid complex (SHC), to assess their probable associations with age and gender, and to investigate the prevalence of it in a wide range of a Turkish sub-population by using cone beam computed tomography (CBCT).Materials and Methods: The CBCT images of the 1000 patients were evaluated retrospectively. The length, thickness, sagittal and transverse angulations, morphological variations and ossification degrees of SHC were evaluated on multiplanar reconstructions (MPR) adnd three-dimensional (3D) volume rendering (3DVR) images. The data were analysed statistically by using nonparametric tests, Pearson's correlation coefficient, Student's t test, c2 test and one-way ANOVA. Statistical significance was considered at p < 0.05.Results: It was determined that 684 (34.2%) of all 2000 SHCs were elongated (> 35 mm). The mean sagittal angle value was measured to be 72.24° and the mean transverse angle value was 70.81°. Scalariform shape, elongated type and nodular calcification pattern have the highest mean age values between the morphological groups, respectively. Calcified outline was the most prevalent calcification pattern in males. There was no correlation between length and the calcification pattern groups while scalariform shape and pseudoarticular type were the longest variations.Conclusions: We observed that as the anterior sagittal angle gets wider, SHC tends to get longer. The most observed morphological variations were linear shape, elongated type and calcified outline pattern. Detailed studies on the classification will contribute to the literature. (Folia Morphol 2018; 77, 1: 79-89). [ABSTRACT FROM AUTHOR]

Objectives: A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (LSTM) to detect the separated endodontic instruments on dental radiographs., Methods: Panoramic radiographs from the hospital archive were retrospectively evaluated by two dentists. A total of 915 teeth, of which 417 are labeled as "separated instrument" and 498 are labeled as "healthy root canal treatment" were included. A total of six deep learning models, four of which are some varieties of CNN (Raw-CNN, Augmented-CNN, Gabor filtered-CNN, Gabor-filtered-augmented-CNN) and two of which are some varieties of LSTM model (Raw-LSTM, Augmented-LSTM) were trained based on several feature extraction methods with an applied or not applied an augmentation procedure. The diagnostic performances of the models were compared in terms of accuracy, sensitivity, specificity, positive- and negative-predictive value using 10-fold cross-validation. A McNemar's tests was employed to figure out if there is a statistically significant difference between performances of the models. Receiver operating characteristic (ROC) curves were developed to assess the quality of the performance of the most promising model (Gabor filtered-CNN model) by exploring different cut-off levels in the last decision layer of the model., Results: The Gabor filtered-CNN model showed the highest accuracy (84.37 ± 2.79), sensitivity (81.26 ± 4.79), positive-predictive value (84.16 ± 3.35) and negative-predictive value (84.62 ± 4.56 with a confidence interval of 80.6 ± 0.0076. McNemar's tests yielded that the performance of the Gabor filtered-CNN model significantly different from both LSTM models ( p < 0.01)., Conclusions: Both CNN and LSTM models were achieved a high predictive performance on to distinguish separated endodontic instruments in radiographs. The Gabor filtered-CNN model without data augmentation gave the best predictive performance.