Alteration of salivary LPO, MDA, LDH, glutathione, GPx, SOD and vitamins in oral submucous fibrosis: A three-level meta-analysis study.

This study aims to investigate the alteration of salivary biomarker profiling in the development of oral submucous fibrosis (OSMF) and to explore the influence of saliva in the diagnosis of OSMF. A systematic search of published articles using the PRISMA guidelines was conducted to identify relevant studies on OSMF and saliva. All eligible studies, including case-control, cross-sectional studies, cohort, and pilot studies, contained the evaluation of salivary biomarker profiling in patients with OSMF. Salivary biomarker data from 28 selected articles were categorized into nine groups, and their mean values were determined. A three-step meta-analysis was performed by grouping salivary biomarker profiling into more heterogeneous categories based on OSMF classification, considering functional, histological, and clinical grading. The salivary biomarker profiling analysis revealed significant alterations in all markers, indicating their efficacy in OSMF diagnosis. Subgroup analyses highlighted significant associations in oxidative stress and protein with increased mean values, particularly emphasizing lipid peroxidase (LPO), malondialdehyde (MDA), and lactate dehydrogenase (LDH). Conversely, decreased mean values were observed in glutathione, glutathione peroxidase (GPx), superoxide dismutase (SOD), and vitamins. Notably, OSMF grading analysis demonstrated a significant difference in weighted effect sizes for histological grading, particularly in stage IV. The study underscores the alteration of specific salivary biomarkers, particularly those associated with LPO, MDA, LDH, glutathione, GPx, SOD, and vitamins, in diagnosing and grading OSMF.

Accuracy of the London Atlas to estimate the age of legal majority in a sample of the Amazon Regio.

Background: Estimating the age of majority is a challenging task in forensic odontology, especially because the third molars are usually the only developing teeth between the ages of 16 and 21 years. The London Atlas emerged as an alternative to estimate age using dental development, eruption and deciduous root resorption as parameters. The method has performed well in young age categories, while its performance for age estimation via third molars has been disputed. The present study tested the performance of the London Atlas to estimate the age of legal majority in a sample of individuals from the Amazon Region. Material and Methods: The sample consisted of 1.256 panoramic radiographs of women (n = 694) and men (n = 562) between 16 and 22.9 years. The method was applied to the maxillary (#28) and mandibular (#38) left third molars. For comparative purposes, the sample was divided into seven age groups: 16├ 16.99; 17├ 17.99; 18├ 18.99; 19├ 19.99; 20├ 20.99; 21├ 21.99; and 22├ 22.99 years. Chronological and estimated ages were compared descriptively by means of mean absolute errors (MAE) and root mean squared errors (RMSE), as well as through Receiver Operating Characteristic (ROC) curves and their area under the curve (AUC). Results: The MAE of the age estimates using tooth #28 was 1.76 years for females and 1.52 years for males. When the tooth #38 was used, the MAE for the females and males were 1.68 and 1.51 years, respectively. The MAE and RMSE increased in ascending order between age categories. Tooth #28 led to 74% of correct classifications around the age of legal majority, while tooth #38 reached 77%. The area under the curve was 0.75 for tooth #28 and 0.73 for tooth #38. Conclusions: The London Atlas should be used carefully to estimate the age of legal majority and not as a single method when the age threshold is 18 years. Key words:Age determination by teeth, forensic dentistry, forensic science, third molar.

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.

Dental age estimation in Indonesian adults: An investigation of the maxillary canine pulp-to-tooth volume ratio using cone-beam computed tomography.

Purpose: This study was performed to develop a linear regression model using the pulp-to-tooth volume ratio (PTVR) ratio of the maxillary canine, assessed through cone-beam computed tomography (CBCT) images, to predict chronological age (CA) in Indonesian adults. Materials and Methods: A sample of 99 maxillary canines was collected from patients between 20 and 49.99 years old. These samples were obtained from CBCT scans taken at the Universitas Padjadjaran Dental Hospital in Indonesia between 2018 and 2022. Pulp volume (PV) and tooth volume (TV) were measured using ITK-SNAP, while PTVR was calculated from the PV/TV ratio. Using RStudio, a linear regression was performed to predict CA using PTVR. Additionally, correlation and observer agreement were assessed. Results: The PTVR method demonstrated excellent reproducibility, and a significant correlation was found between the PTVR of the maxillary canine and CA (r=-0.74, P<0.01). The linear regression analysis showed an R2 of 0.58, a root mean square error of 5.85, and a mean absolute error of 4.31. Conclusion: Linear regression using the PTVR can be effectively applied to predict CA in Indonesian adults between 20 and 49.99 years of age. As models of this type can be population-specific, recalibration for each population is encouraged. Additionally, future research should explore the use of other teeth, such as molars.

The radiographic diversity of dental patterns for human identification - Systematic review and meta-analysis.

This study aimed to revisit the scientific literature related to the diversity of dental patterns observed in radiographs. The rationale was to find evidence to support dental human identifications. A systematic review was performed following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Strategic search was accomplished in five electronic data sources (SciELO, Medline/Pubmed, Scopus, Open Grey and OATD) were searched. The study model of choice was observational analytical cross-sectional. The search resulted 4.337 entries. The sequential screening based on title, abstract and full-text reading led to 9 eligible studies (n = 5.700 panoramic radiographs) published between 2004 and 2021. Studies from Asian countries were predominant (e.g., South Korea, China, and India). All the studies showed low risk of bias (measured according to the Johanna Briggs Institute's critical appraisal tool for observational cross-sectional studies). Morphological, therapeutic, and pathological identifiers were charted from radiographs to create dental patterns across studies. Six studies (n = 2.553 individuals) had similar methodology and outcome metrics and were included in the quantitative analysis. A meta-analysis was performed and revealed a pooled diversity of the human dental pattern of 0.979 combining maxillary and mandibular teeth. The additional subgroup analysis with maxillary and mandibular teeth have a diversity rate of 0.897 and 0.924, respectively. The existing literature shows that human dental patterns are highly distinctive, especially if morphological, therapeutic and pathological dental features are combined. The diversity of dental identifiers found in the maxillary, mandibular and combined arches is hereby corroborated by this meta-analyzed systematic review. These outcomes support applications for evidence-based human identification.

3D segmentation of dental crown for volumetric age estimation with CBCT imaging.

In adult dental age estimation, segmentation of dental volumetric information from different tooth parts using cone-beam computed tomography (CBCT) has proven beneficial in improving the regression model reliability. This segmentation method can be expanded in the crown part since the volumetric information in the crown is affected by attrition in the enamel and secondary dentine in the dentine and pulp chamber. CBCT scans from 99 patients aged between 20 and 60 were collected retrospectively. A total of 80 eligible teeth for each tooth type were used in this study. The enamel to dentine volume ratio (EDVR), pulp to dentine volume ratio (PDVR) and sex were used as independent variables to predict chronological age (CA). The EDVR was not affected by PDVR. The highest R2 was calculated from the maxillary canine (R2 = 0.6). The current approach in crown segmentation has proven to improve model performance in anterior maxillary teeth.

Regressive changes of crown-root morphology and their volumetric segmentation for adult dental age estimation.

Cone-beam computed tomography (CBCT) enables the assessment of regressive morphological changes in teeth, which can be used to predict chronological age (CA) in adults. As each tooth region is known to have different correlations with CA, this study aimed to segment and quantify the sectional volumes of the tooth crown and root from CBCT scans to test their correlations with the chronological age (CA). Seventy-five CBCT scans from individuals with age between 20 and 60 years were collected retrospectively from an existing database. A total of 192 intact maxillary anterior teeth fulfilled the eligibility criteria. The upper tooth volume ratio (UTVR), lower tooth volume ratio (LTVR), and sex were used as predictor variables. The UTVR and LTVR parameters were both found to be differently correlated to CA and independent from each other. Regression models were derived from each tooth, with the highest R2 being the maxillary lateral incisor (R2  = 0.67). Additional single predictor models using each ratio were capable of reliably predicting the CA. The segmentation approach in volumetric adult dental age estimation proved to be beneficial in enhancing the reliability of the regression model.

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.

CBCT in dental age estimation: A systematic review and meta analysis.

OBJECTIVES: This study aimed to investigate the reproducibility of dental age estimation methods in cone beam computed tomography (CBCT) and the correlation between dental (DA) and chronological (CA) ages. METHODS: The scientific literature was searched in six databases (PubMed, Scopus, LILACS, Web of Science, SciELO, and OATD). Only observational studies were selected. Within each study, the outcomes of interest were (I) the quantified reproducibility of the method (κ statistics and Intraclass correlation coefficient); and (II) the correlation (r) between the dental and chronological ages. A random-effect three-level meta-analysis was conducted alongside moderator analysis based on methods, arch (maxillary/mandibular), population, and number of roots. RESULTS: From 671 studies, 39 fulfilled the inclusion criteria, with one study reporting two different methods. The methods used in the studies were divided into metric (n = 17), volumetric (n = 20), staging (n = 2), and atlas (n = 1). All studies reported high examiner reproducibility. Group 1 (metric and volumetric) provided a high inverse weighted r ([Formula: see text] = -0.71, CI [-0.79,-0.61]), and Group 2 (staging) provided a medium-weighted r ([Formula: see text] = 0.49, CI [0.44, 0.53]). Moderator analysis on Group one did not show statistically significant differences between methods, tooth position, arch, and number of roots. An exception was detected in the analysis based on population (Southeast Asia, [Formula: see text] = -0.89, CI [-0.94,-0.81]). CONCLUSION: There is high evidence that CBCT methods are reproducible and reliable in dental age estimation. Quantitative metric and volumetric analysis demonstrated better performance in predicting chronological age than staging. Future studies exploring population-specific variability for age estimation with metric and volumetric CBCT analysis may prove beneficial.

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.

Effect of Lower Third Molar Segmentations on Automated Tooth Development Staging using a Convolutional Neural Network.

Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages - allocated by human observers - overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear κ of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation.