Accuracy of the third molar maturity index (I3M) for diagnosing the legal majority of young southern Brazilians.

Background: The numerous techniques for identifying adulthood require research testing the accuracy of each method in different populations. This study verified the accuracy of the third molar maturity index (I3M) proposed by Cameriere et al. (2008) for diagnosing the age of majority in a southern Brazilian population sample. Material and Methods: Panoramic radiographs of patients with dental element 38 treated at the School of Dentistry of the University of Passo Fundo (UPF), RS, Brazil, were analyzed. The patients were separated into age groups between 15.00 and 22.99 years. The Cameriere (2008) method was applied to each radiograph. The study sample comprised 671 individuals, with 385 women (mean age 19.67 ± 2.05) and 286 men (mean age 19.5 ± 2.11). Results: The original cut-off value of I3M≤0.08 classified individuals younger and older than 18 years. ROC curve plotting resulted in an overall accuracy of 0.69 and 0.84 for women and men, respectively. The most favorable cut-off value for southern Brazilian men was 0.06, and women showed better results with an I3M adjusted to 0.13. The new cut-off values produced an accuracy of 0.78 for women and 0.84 for men. The original cut-off point to the I3M (0.08) was not the most appropriate for the studied sample. Conclusions: Thus, index adjustments to 0.13 for women and 0.06 for men may improve method performance among southern Brazilian individuals. Key words:Molar, third, radiography, panoramic, forensic dentistry, age groups, imputability.

The expression of TNF-α in recurrent aphthous stomatitis: A systematic review and meta-analysis.

OBJECTIVE: The pathogenesis of recurrent aphthous stomatitis (RAS) is related to an increase of pro-inflammatory cytokine, namely tumor necrosis factor α (TNF-α). This cytokine plays an important role in the development of ulcer lesions, both in saliva, tissues and blood. This systematic review analyzed the differences of TNF-α in lesions, salivary and blood and can be used as a reliable method of diagnosis for RAS. METHODS: A comprehensive search of PubMed, Scopus databases, Web of Science, Scielo, Google Scholar and Embase with keywords. The inclusion criteria were studies that assessed the saliva, serum, and RAS lesion, with the outcome reporting the mean of saliva, serum and tissue expression of TNF-α. The risk of bias was also assessed. RESULT: Healthy individuals showed significantly lower TNF-α than RAS (SMD = -1.517, 95% CI [-2.25, -0.78]). Although there is a significant difference between sample (i.e., saliva, serum) and detection type (i.e., cytometry bead array, ELISA), both methods can detect a significant difference in TNF-α between healthy individuals and RAS patients. CONCLUSIONS: The TNF-α is a useful diagnostic marker for RAS. We encourage saliva to detect changes in TNF-α during ulceration as it provides accuracy, reliability, and non-invasive procedure compared to a blood draw.

Towards fully automated third molar development staging in panoramic radiographs.

Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen's kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.