Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, € Ozy€ urek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. ) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis.Results The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05).Conclusions Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application.

Dental professionals have always been meticulous about infection control due to high risk of cross-contamination during dental procedures. Nevertheless, there is an urgent need to review and revise our current practice of infection control and develop more strict protocols that will prevent nosocomial spread of infection during COVID-19 outbreak and future pandemics. The risk of contamination is high during dental radiography if proper disinfection techniques are not applied. This document provides advice and guidance for infection control when practicing dental radiography during COVID-19 pandemic.

Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

Objectives: The purpose of this study was to evaluate fractal analysis as a tool to quantitatively determine the mandibular trabecular bone changes in patients with chronic renal failure (CRF). Methods: In the present study, fractal analysis was performed using ImageJ (National Institutes of Health, Bethesda, MD) program with box-counting method over panoramic radiographs of 25 patients (14 females and 11 males) with CRF and 26 healthy individuals (14 females and 12 males) as a control group. The fractal dimension (FD) values of the patients and healthy individuals were compared. In addition, average biochemical parameters [parathyroid hormone (PTH), calcium (Ca), phosphorus (P), product of Ca and P levels (CaxP), alkaline phosphatase (ALP), vitamin D] of the patients with CRF, as measured during the 3 months before the panoramic radiographs, were compared with FD values. Results: According to the results, FD values of the patients with CRF were found to be statistically lower than the control group (p , 0.05). The average PTH levels of the patients with CRF were 416.16 ± 310.3 pg ml 21 ; average Ca levels were 8.94 ± 1.2 mg dl 21 ; average P levels were 5.76 ± 1.7 mg dl 21 ; average CaxP values were 51.12 ± 15.03; average ALP levels were 83.44 ± 36.8 U l 21 ; and the average vitamin D values were 19.43 ± 9.7 ng ml 21 . In addition, there was no significant correlation between FD values and the biochemical parameters of the patients, and there was no correlation between age, gender and FD. Conclusions: The FD values of the patients with CRF were lower than those of the controls. This finding suggests that FD analysis might be a promising simple and cost-effective tool for evaluating trabecular bone structure.

This study shows that PP is not an uncommon anatomic variation and is a natural incidental finding on CBCT.

The anatomical structure of sella turcica can be studied effectively in CBCT images. Linear dimensions and shape of sella turcica in the current study can be used as reference standards for further investigations.

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Objective: The aim of this study was to assess the morphology of the sella turcica and measure its size in cleft and noncleft subjects. Material and Methods: Cone-beam computed tomography (CBCT) images of 54 individuals (29 males; 25 females) with cleft and 85 (22 males; 63 females) without cleft were used for this study. Syndromic patients with cleft(s) were not included because of possible additional endocrinological and/or morphological disorders. Linear measurements included length, depth, and diameter. The shape of the sella turcica was analyzed in the cleft and noncleft groups. An independent t test was conducted to evaluate differences between genders and groups. One-way ANOVA was used to compare age groups. Results: The length (p < 0.001) of the sella turcica was smaller in noncleft subjects than in cleft subjects. Diameter (p = 0.014) and depth (p = 0.005) showed as constantly increasing from an age <15 to >25 years in the overall assessment. The distribution of the shape of the sella turcica differed significantly between groups (p < 0.001). Conclusions: In this study, CBCT was used to assess the morphology of the sella turcica. A majority of the subjects with cleft had a flattened sella turcica compared to that of the control group. A shorter length of the sella turcica was more evident in the cleft subjects than in the control group.