Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. CBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. The success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. Although PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.

Aim: The purpose of this study was to obtain data concerning growth factor release within liquid and solid platelet-rich fibrin (PRF) matrices and to estimate the amount of potential interindividual variations as a basis for further preclinical and clinical trials. Therefore, we aimed to determine possible differences in the release of growth factors between liquid and solid PRF. Materials and Methods: Blood samples obtained from four subjects were processed to both liquid and solid PRF matrices using a standard centrifugation protocol. Five growth factors (vascular endothelial growth factor, VEGF; epidermal growth factor, EGF; platelet-derived growth factor-BB, PDGF-BB; transforming growth factor-β1, TGF-β1; and matrix metallopeptidase 9, MMP-9) have been evaluated at six time points by ELISA over a total observation period of 10 days (1 h, 7 h, 1 d, 2 d, 7 d, and 10 d). Results: Growth factor release could be measured in all samples at each time point. Comparing liquid and solid PRF matrices, no significant differences were detected (p > 0.05). The mean release of VEGF, TGFβ-1, PDGF-BB, and MMP-9 raised to a peak at time point five (day 7) in both liquid and solid PRF matrices. VEGF release was lower in liquid PRF than in solid PRF, whereas those of PDGF-BB and MMP-9 were higher in liquid PRF than in solid PRF at all time points. EGF had its peak release already at time point two after 7 h in liquid and solid matrices (hour 7 EGF solid: mean = 180 pg/mL, SD = 81; EGF liquid: mean = 218 pg/mL, SD = 64), declined rapidly until day 2, and had a second slight peak on day 7 in both groups (day 7 EGF solid: mean = 182 pg/mL, SD = 189; EGF liquid: mean = 81 pg/mL, SD = 70). Conclusions: This study detected growth factor release within liquid and solid PRF matrices with little variations. Further preclinical trials are needed to precisely analyze the growth factor release in larger samples and to better understand their effects on wound healing in different clinical indications.

Medication-related osteonecrosis of the jaw is a known side-effect of antiresorptive therapy in patients with malignant diseases. Nevertheless, the exact pathogenesis is still unknown and published prevalences show a significant range. The aim of the presented paper was to assess the prevalence of osteonecrosis (ONJ) in breast cancer, prostate cancer, and multiple myeloma patients receiving parenteral antiresorptive therapy. For this reason a PubMed search was performed and 69 matching articles comprising 29,437 patients were included in the analysis. Nine-hundred fifty-one cases of jaw necrosis were described. The overall ONJ-prevalence was 2.09% in the breast cancer group, 3.8% in the prostate cancer group, and 5.16% for multiple myeloma patients.

To explore the global trends in blended learning in undergraduate dental education during the COVID pandemic and during the recovery phase by engaging with the students and faculty and evaluate the...

The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm’s robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.