Studying eruption patterns of mandibular third molars for forensic age assessment: Introducing current reference data from a population of black South Africans.

INTRODUCTION: Evaluation of the eruption of mandibular third molars in orthopantomograms (OPGs) is a method of forensic age assessment. The objective of our study was to provide valid reference data for this trait within a population of black South Africans. The study was guided by the criteria for reference studies in age assessment. MATERIALS AND METHODS: A study population from Pretoria, South Africa comprising 670 OPGs obtained from 338 black females and 332 black males aged between 15.00 and 25.97 years was analysed. All OPGs were performed for medical indication during the period from 2011 to 2022 and were retrospectively evaluated. From the 670 OPGs, a total of 1021 mandibular third molars were evaluated. The assessment of mandibular third molars was conducted using the staging scale presented by Olze et al. in 2012. Two experienced dentists evaluated the OPGs independently of each other. If the two examiners diverged in their assessments, a consensus stage was assigned. RESULTS: As expected, the mean, median and minimal age increased with higher stages for both teeth and both sexes. The minimum age recorded for stage D, indicating complete tooth eruption, was 15.79 years in females and 16.62 years in males. CONCLUSION: As it is the case for previous reference studies in other countries, placing exclusive reliance on the evaluation of mandibular third molar eruption is inadequate for ascertaining the age of majority among Black South Africans. Future studies need to examine if our results are transferable to other countries in Sub-Saharan Africa.

Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

Characteristics of CBCT versus panoramic signs of the surrounding bone of molar root protrusions into the maxillary sinus.

The study aimed to (a) investigate the amount and characteristics of the surrounding bone of protruded molar roots into the maxillary sinus using cone-beam computed tomography (CBCT) and (b) assess the correlation between the amount of bone with panoramic high-risk signs. Radiographs of 408 roots protruding beyond the sinus floor were evaluated. Axial CBCT images were used to investigate then classify eight characteristics of surrounding bone: no bone; bone < half the root girth in the proximal or buccal-palatal aspect; bone covering half the root girth in the proximal or buccal-palatal aspect; bone > half the root girth in the proximal or buccal-palatal aspect; and, complete bone. These were then grouped into four degrees of bone support: no bone; bone ≤ half the root girth; bone > half the root girth; and, complete bone. Panoramic signs were subclassified as: projection of root; interruption of the sinus floor; darkening of the root; upward curving of the sinus floor; absence of periodontal ligament space; and, absence of the lamina dura. Correlation between the degree of bone and the panoramic signs was evaluated using the Chi-square or Fisher's exact tests. Positive and negative predictive values, sensitivity, specificity, accuracy, and receiver operating characteristic analysis were calculated. Complete bone support was the most common. 'Projection of root' had a high negative predictive value and sensitivity. 'Absence of the periodontal ligament space and lamina dura' had a high positive predictive value, specificity, accuracy, and area under the curve. These two signs were significantly correlated with the degree of bone support.

Combining public datasets for automated tooth assessment in panoramic radiographs.

OBJECTIVE: Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. MATERIALS AND METHODS: Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. RESULTS: The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model's performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. CONCLUSIONS: The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.

Root resorption of primary molars and dental development of premolars in children with Osteogenesis Imperfecta medicated with bisphosphonates, grouped according to age and gender.

BACKGROUND: Osteogenesis imperfecta (OI) is an inherited disorder characterized by bone fragility and skeletal alterations. The administration of bisphosphonates (BPs) to patients with OI reduces pain, thereby improving their quality of life. The main mechanism of action of BPs is the inhibition of osteoclast action. In the oral cavity of children with OI during growth and development, physiological processes that require the function of osteoclasts occur. The aim of this investigation was to study the dental development of premolars and the root resorption of primary molars in children with OI medicated with BPs according to age and sex. METHODS: An observational and analytical study was designed. The study sample consisted of 26 6- to 12-year-old children with a confirmed diagnosis of OI treated with BPs with available panoramic radiographs. The control group consisted of 395 children with available panoramic radiographs. Both groups were divided into subgroups according to sex and age. The third quadrant was studied, focusing on the first left temporary molar (7.4), the second left temporary molar (7.5), the first left permanent premolar (3.4) and the second left permanent premolar (3.5). The Demirjian method was used to study the dental development of 3.4 and 3.5, and the Haavikko method was used to study the root resorption of 7.4 and 7.5. The Mann‒Whitney U test was used for comparisons, and p < 0.05 indicated statistical significance. RESULTS: The mean chronological age of the 421 patients was 9.21 years (95% CI 9.05-9.37). The sample was reasonably balanced by sex, with 52.5% (221 patients) boys versus 47.5% (200 patients) girls. Delayed exfoliation and tooth development were described in children with OI (p = 0.05). According to sex, the root resorption of primary molars and tooth development were significantly lower in boys in both groups and in girls in the OI group, but the differences between the age groups were not significant. CONCLUSIONS: Children with OI treated with BPs exhibit delayed dental development of the premolars and delayed root resorption of the primary molars. Boys exhibited delays in both variables, but the differences by age subgroup were not significant. These clinical findings support the importance of clinically and radiographically monitoring the dental development and root resorption of primary teeth in children with OI treated with BPs to avoid alterations of the eruptive process.

Features of Dental Anomaly Patterns in Finnish children as seen in panoramic radiographs at the late mixed stage.

OBJECTIVE: To describe the prevalence of the developmental abnormalities involved in Dental Anomaly Patterns (DAP) and investigate their co-occurrence in an age cohort of children with late mixed dentition. MATERIAL AND METHODS: Retrospective, register-based study focused on 1315 panoramic radiographs of children aged 8.5-10.5 years. The features examined were absent teeth, peg-shaped maxillary lateral incisor, delayed dental age, infraocclusion of primary molars, transposition and distal angulation of unerupted mandibular second premolar. RESULTS: Feature involved in DAP was detected in 29.8% of the children, most common being infraocclusion of primary molars (17.5%), followed by absent teeth (8.4%), delayed dental age (7.6%), distal angulation of unerupted mandibular second premolar (7.3%), peg-shaped maxillary lateral incisor (2.4%) and transposition (0.5%). Two DAP features occurred together in 4.7% of children, while three occurred in 0.7%. Infraocclusion (p=.040) and absent teeth (p=.001) occurred more commonly in girls. Phenotypic variations in maxillary lateral incisor more often occurred together (p=.004). Absent teeth, peg-shaped maxillary lateral incisor and delayed dental age more often occurred together (p<.01) as did transposition and absent teeth (p=.016). CONCLUSION: Almost third of the children had dental developmental abnormalities involved in DAP. Absent teeth, peg-shaped lateral incisors and delayed dental age more often occurred together.

Dental anomalies in an Albanian orthodontic sample: a retrospective study.

BACKGROUND: To evaluate the prevalence and the distribution of dental anomalies in an Albanian orthodontic sample. METHODS: For this retrospective study, a sample of panoramic radiographs (PR) of n. 779 (456 F and 323 M, mean age of 15.1 ± 5.5 years) Albanian subjects with no genetic syndromes or craniofacial malformations (e.g., cleft lip/palate), history of extraction, trauma or previous orthodontic treatment drawn from the archives of the University Dental Clinic Our Lady of Good Counsel, was examined. The inclusion criteria were: subjects from 8 to 30 years of age, good quality of PRs in order to allow the assessment of crown and root development. For different dental anomalies, both the prevalence and the association were evaluated by using Chi-square test (p < 0.05). RESULTS: 24.4% of the sample had at least one dental anomaly and 4.6% had more than one. The following dental anomalies were most prevalent: hypodontia (9.8%), dental impaction (7.6%), and ectopic eruption (5.3%). No statistically significant correlation was found between males and females (p > 0.05). CONCLUSIONS: The most common anomalies in this group are found to be those of number and position, and only radiological investigations can reveal either of these anomalies. Early diagnosis of dental anomalies can contribute to prevent their complications and to establish the most suitable therapy to achieve a functional occlusion.

Is there an association between molar incisor hypomineralization and developmental dental anomalies? A case-control study.

BACKGROUND: The aim of this study was to determine whether there is any association between molar incisor hypomineralization and developmental dental anomalies. METHODS: Two pediatric dentists evaluated panoramic radiographs of 429 children aged 8-14 years with molar incisor hypomineralization (study group) and 437 children without molar incisor hypomineralization (control group) in terms of developmental dental anomalies. Twelve different developmental dental anomalies were categorized into four types: size (microdontia, macrodontia); position (ectopic eruption of maxillary permanent first molars, infraocclusion of primary molars); shape (fusion, gemination, dilaceration, taurodontism, peg-shaped maxillary lateral incisors); and number (hypodontia, oligodontia, hyperdontia) anomalies. RESULTS: No significant difference was observed in the frequencies of developmental dental anomalies between the study and control groups in total, females, and males (p > 0.05). A statistically significant difference was found between the distribution of developmental size, position, shape, and number anomalies between the study and control groups (p = 0.024). The most common anomaly in both groups was hypodontia (6.3% and 5.9%, respectively). There was a significant difference between the study and control groups in terms of subtypes of shape anomaly in all children and females (p = 0.045 and p = 0.05, respectively). CONCLUSIONS: While a significant difference was observed between the distributions of types of developmental dental anomalies between individuals with and without molar incisor hypomineralization, there was no difference in terms of the frequency of developmental dental anomalies.

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study.

BACKGROUND: Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance. METHODS: The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05). RESULTS: Sensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001). CONCLUSIONS: The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.

Quantitative level determination of fixed restorations on panoramic radiographs using deep learning.

AIM: Although many studies in various fields employ deep learning models, only a few such studies exist in dental imaging. The present article aims to evaluate the effectiveness of convolutional neural network (CNN) algorithms for the detection and diagnosis of the quantitative level of dental restorations using panoramic radiographs by preparing a novel dataset. MATERIALS AND METHODS: 20,973 panoramic radiographs were used, all labeled into five distinct categories by three dental experts. AlexNet, VGG-16, and variants of ResNet models were trained with the dataset and evaluated for the classification task. Additionally, 10-fold cross-validation (ie, 9 folds were separated for training and 1 fold for validation) and data augmentation were carried out for all experiments. RESULTS: The most successful result was shown by ResNet-101, with an accuracy of 92.7%. Its macro-average AUC was also the highest, at 0.989. Other accuracy results obtained for the dataset were 75.5% for AlexNet, 85.0% for VGG-16, 92.1% for ResNet-18, 91.7% for ResNet-50, and 92.1% for InceptionResNet-v2. CONCLUSIONS: An accuracy of 92.7% is a very promising result for a computer-aided diagnostic system. This result proved that the system could assist dentists in providing supportive preliminary information from the moment a patient's first panoramic radiograph is taken. Furthermore, as the introduced dataset is powerful enough, it can be relabeled for different problems and used in different studies.

Assessment of the correlation between mandibular radiomorphometric patterns and dual-energy X-ray absorptiometry measurements for screening bone mineral density.

The objective of this study was to investigate the correlation between scores for femoral and lumbar spine bone mineral density (dual-energy X-ray absorptiometry [DXA]) and visual, qualitative mandibular bone pattern assess¬ments (mandibular cortical index, trabecular bone pattern, and visual mandibular cortical width) as well as age and body mass index. Three trained observers evaluated 200 panoramic radiographs and 200 femoral and lumbar spine DXA measurements from 100 male and 100 female participants. The κ test showed an acceptable agreement among observers (0.73; P = 0.003). The Shapiro-Wilk test revealed that the variables were not normally distributed, so the Spearman correlation test was used. The mean age of the sample was 60.7 (13.9) years, and 86.0% of the patients were White. There were inverse correlations between the mandibular cortical index and the spine T-score in men, women, and the total sample as well as between the spine Z-score in the total sample. An inverse correlation was observed between the trabecular bone pattern and the spine T- and Z-scores in women and the total sample. The results also showed inverse correlations between the visual mandibular cortical width and all parameters analyzed in men, women, and the total sample except for the femur T-score and spine T- and Z-scores in men. Body mass index was correlated with all DXA parameters. Age was inversely correlated with femur T-scores in men and women but not with spine DXA values in men. The results suggest that qualitative assessments of radiomorphometric patterns on panoramic radiographs correlate with DXA values and therefore are suitable for screening patients at risk of developing low bone mineral density.

Evaluation of Medication-Related Osteonecrosis of the Jaw (MRONJ) in Terms of Staging and Treatment Strategies by Dental Students at Different Educational Levels.

Background: The role of medication-related osteonecrosis of the jaw (MRONJ) as a dento-maxillo-facial pathology is becoming increasingly important due to its growing prevalence. The success of preventive and therapeutic measures relies mainly on the dentist's ability to correctly diagnose the disease. Methods: The aim of this study was to evaluate the skills of dental students of different educational levels in choosing the correct stage, diagnostics, and treatment option for MRONJ based on clinical and radiographic imaging (panoramic radiograph, CBCT). The study was designed as a cross-sectional cohort study. Twenty dental students were asked to complete a questionnaire in their third and fifth year of studies in which they had to correctly stage the disease, choose the radiological diagnostics and recommend the treatment. The control group contained experienced oral and maxillofacial surgeons. Results: With an overall performance of 59% (third year: 145.2/248 points; fifth year: 145.3/248 points), no statistically significant difference between the educational levels could be observed. The classification based on CBCT imaging was significantly more often correct compared to panoramic radiographs (p < 0.001). Conclusions: This study highlights students' lack of knowledge in staging, diagnostics, and treatment of MRONJ, even though the CBCT positively affected decision-making. No significant increase in knowledge could be confirmed through clinical education. This study highlights the need for students to catch up on MRONJ diagnostics and treatment planning. Further expansion of teaching in this disease's context and X-ray diagnostics is needed.

Periapical Lesions Associated with Demographic Variables, Dental Conditions, Systemic Diseases, and Habits.

AIM AND BACKGROUND: The aim of this study was to analyze the prevalence of periapical lesions and possible associations with demographic, dental conditions, systemic diseases, and habit variables in patients from a private Brazilian university. MATERIALS AND METHODS: Data from 452 patients of both sexes, aged 18-78 years, from a Brazilian university were evaluated. Panoramic radiographs were analyzed, and the presence of periapical lesions was recorded. In these cases, the dental condition was assessed for the presence or absence of endodontic treatment. Medical records provide information related to general health (diabetes, cardiovascular diseases, altered cholesterol, autoimmune diseases, gallstones, or kidney stones) and habits (smoking or alcoholism). The data were descriptively analyzed, and then logistic regression and the Wald test were performed in Stata/SE v.14.1. RESULTS: A rate of 58.4% of participants were women, and the mean age was 36.4 (±14.6) years. Periapical lesions prevailed in 193 (42.7%) patients, and 281 (72.4%) teeth were not previously endodontically treated. Mandibular first molars (19%) and men (48.9%) were the most affected. The adjusted model showed that the age group of 26-45 years was 5 times more likely [odds ratio (OR) = 5.01; 95% confidence interval (CI): 2.85-8.82] to have lesions than those aged ≤25 years. Participants aged above 46 years were 19.1 times more prone (OR = 19.1; 95% CI: 10.2-36.0) to morbidity than younger ones (≤25 years). CONCLUSION: The studied sample showed that periapical lesions were more prevalent in males, in mandibular molars, and without prior endodontic treatment. There was no observed association between periapical lesions and comorbidities or habits; however, a significant correlation occurred with advancing age. CLINICAL SIGNIFICANCE: Comorbidities and habits were not associated with the presence of periapical lesions. However, aging has been identified as a significant risk factor for the development of periapical lesions. This finding is clinically relevant as it highlights the importance of monitoring and maintaining oral health in patients with advanced age. How to cite this article: dos Santos VC, de Oliveira Kublitski PM, Marques da Silva B, et al. Periapical Lesions Associated with Demographic Variables, Dental Conditions, Systemic Diseases, and Habits. J Contemp Dent Pract 2023;24(11):864-870.

[Application of Deep Learning in Differential Diagnosis of Ameloblastoma and Odontogenic Keratocyst Based on Panoramic Radiographs].

Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.Transfer learning was employed to train 800 panoramic radiographs in the training set through 5-fold cross validation,and 200 panoramic radiographs in the test set were used for differential diagnosis.Chi square test was performed for comparing the performance among different CNN.Furthermore,7 oral radiologists (including 2 seniors and 5 juniors) made a diagnosis on the 200 panoramic radiographs in the test set,and the diagnosis results were compared between CNN and oral radiologists. Results The eight neural network models showed the diagnostic accuracy ranging from 82.50% to 87.50%,of which EfficientNet b1 had the highest accuracy of 87.50%.There was no significant difference in the diagnostic accuracy among the CNN models (P=0.998,P=0.905).The average diagnostic accuracy of oral radiologists was (70.30±5.48)%,and there was no statistical difference in the accuracy between senior and junior oral radiologists (P=0.883).The diagnostic accuracy of CNN models was higher than that of oral radiologists (P<0.001). Conclusion Deep learning CNN can realize accurate differential diagnosis between ameloblastoma and odontogenic keratocyst with panoramic radiographs,with higher diagnostic accuracy than oral radiologists.

Quantifying the potential of morphological parameters for human dental identification: part 2-selecting the strongest identifiers in mandibular permanent teeth.

The current study aimed to select the best dental morphological identifiers for human identification. Sixty-two panoramic radiographs were collected retrospectively, in which six measurements were performed on all seven mandibular left permanent teeth: tooth length (TL), crown length (CL), root length (RL), crown width (CW), cervical width (CEJW), and root width (RW). Nine length-width ratios were then calculated using these measurements. Three groups of statistics were considered: (1) inter-observer reliability quantified by intra-class correlation (ICC); (2) mean "potential set"; and (3) Spearman correlation. A step-by-step cascade was then established based on selected parameters. In a univariate approach, the following parameters were the best identifiers: TL/CW for tooth 36 (ICC 0.82; mean potential set 13.7%), TL/CEJW for tooth 35 (ICC 0.87; mean potential set 15.2%), and TL/RW for tooth 32 (ICC 0.89; mean potential set 16.0%). The correlations between these three parameters ranged from 0.24 to 0.47. In a multivariate approach, the following parameters had the best identifying capacity: all parameters combined for tooth 31 (mean potential set 8.1%), for tooth 35 (mean potential set 11.9%), and for tooth 32 (mean potential set 16.3%). In conclusion, a single ratio in a specific tooth narrows down the potential set of matches, but the mean potential set remains relatively large. Combining all ratios of a single specific tooth increases the certainty of the match. In particular, tooth 31 was the strongest identifier.

Validation of the Italian, European, North German, Malaysian, and South African black formulas on Cameriere method using panoramic radiographs in Kenyan children.

This study aimed to validate the accuracy of five different formulas based on the Cameriere method for age estimation in Kenyan children. We analyzed panoramic radiographs of 350 Kenyan children (184 males and 166 females) aged between 6 and 13 years. The dental development of each child was estimated by the analysis of the first seven left permanent teeth in the left mandible. Dental age was calculated by the Italian, European, North German, Malaysian, and South African black formulas previously published in peer-review journals. The children's mean chronological age (CA) was 9.34 ± 2.02 years and 9.26 ± 2.01 years for males and females. In males, the South-African formula overestimated CA least, by 0.12 years, followed by the Italian formula by 0.22 years, the European formula by 0.37 years, the Malaysian formula by 0.48 years, and the North-German formula overestimated the most, by 0.57 years. In females, the South-African formula underestimated CA by - 0.12 years. The Italian overestimated by 0.12 years, followed by the North-German formula by 0.29 years, the European formula by 0.31 years, and the Malaysian formula by 0.40 years. The Italian formula provided the best accuracy in the absolute difference within ± 1 year, by 75.72% and 75%, following the North-German formula by 66.47% and 70.27% for males and females, respectively. Therefore, the findings suggest that the Italian formula best estimates dental age in Kenyan children.

Dental Screening Including Panoramic Radiograph for Gorlin-Goltz Syndrome in Patients With Multiple Basal Cell Carcinomas.

PURPOSE: To answer the following clinical research question: "Among patients with multiple basal cell carcinomas (mBCCs), can panoramic radiograph (PaR) facilitate the diagnosis of Gorlin-Goltz syndrome (GGS)?" METHODS: This retrospective study enrolled mBCCs subjects who presented to a German tertiary care center between 1 January 2015 and 31 December 2021. The primary predictor was presence of syndromic mBCCs, and the main outcomes were jaw cysts and odontogenic keratocysts (OKCs). Descriptive, bi- and multivariate statistics, diagnostic test evaluation, and number needed to screen (NNS) were computed at α = 95%. RESULTS: The sample comprised 527 mBCCs patients (36.1% females; 6.8% GGS; 5.5% OKCs; mean age, 74.5 ± 15.8 years [range, 15-102]). There was a significant association between syndromic mBCCs and jaw cysts (P < .0001; NNS = 2 [95% CI, CI, 1.1 to 1.4]). In the adjusted logistic model, PaR identified GGS via radiographic diagnosis of jaw cysts in case of 1) age ≤ 35 years, 2) ≥ 5 BCCs, and 3) ≥ 1 high-risk BCCs. Nearly every jaw cyst identified by PaR was OKCs (P = .01; 95% CI, 3.1 to 3,101.4; NNS = 1.3 [95% CI, .9 to 2]). The post hoc power was 100%. CONCLUSIONS: Dental screening with the use of PaR for mBCCs patients, especially those aged ≤35 years, or with ≥5 BCCs, or ≥1 high-risk BCCs, may be helpful in detection and identification of GGS through recognition of OKCs.

Evaluation of associations between condylar morphology, ramus height, and mandibular plane angle in various vertical skeletal patterns: a digital radiographic study.

BACKGROUND: To evaluate condylar morphology, ramus height, and asymmetry indexes in patients with different vertical skeletal patterns and to determine the association between condylar and ramal measurements with the mandibular plane angle. METHODS: Dental panoramic radiographs of 60 patients with different skeletal patterns were evaluated. According to the cranial base (Sella-Nasion)-mandibular plane (SN-MP) angle, the patients were divided into three groups: normal angle (NA), low angle (LA), and high angle (HA). The condylar area, condylar perimeter, condylar heights, and ramus height were measured, and the asymmetry index value of each measurement was calculated. A one-way analysis of variance as well as a post hoc Tukey and Kruskall-Wallis tests were used to determine intergroup differences. Pearson's correlation coefficient was used to determine the relationship between all measurements and SN-MP. RESULTS: The intergroup comparison of condylar area, condylar perimeter, condylar heights, and ramus height measurements showed that the patients in the LA group have statistically significantly greater values compared to those in the HA group. A statistically significant difference was detected between the NA and LA groups only in the condylar area measurements. There was no statistical difference only in the ramus height measurements between the NA and HA groups. Asymmetry index values of the groups were similar. The negative correlations were found between all measurements and the SN-MP angle. CONCLUSION: HA individuals have lower ramus heights and smaller condylar morphologies than NA and LA individuals. In addition, as the SN-MP angle increases, the condyle dimensions and ramus height decrease, and this is a clinically important finding.

Informational content of two-dimensional panoramic radiographs and lateral cephalometric radiographs with respect to the bone volume of intraoral donor regions considering CBCT imaging.

BACKGROUND: To test the hypothesis that cephalometric parameters in two-dimensional routine dental radiographs correlate with the bone volume of intraoral bone donor sites. METHODS: One-hundred and eight radiographs [36 panoramic radiographs (PRs), 36 lateral cephalometric radiographs (LCRs), and 36 cone-beam computed tomography scans (CBCT)] of 36 patients (all three imaging techniques applied according to the needs of treatment planning), were analyzed individually. Cephalometric parameters (PR and LCR) were correlated with the bone volume measurement in three-dimensional CBCT scans in three intraoral donor sites (chin, mandibular retromolar region, and zygomatic alveolar crest). RESULTS: The mean bone volumes measured for the chin were (3.10 ± 1.11 cm³ SD), the mandibular retromolar region (1.66 ± 0.54 cm³ SD), and the zygomatic alveolar crest (0.17 ± 0.04 cm³ SD). Cephalometric parameters were significantly correlated (all p-values < 0.05) with the bone volume in the chin and the mandibular retromolar region. The bone volume of the zygomatic alveolar crest exhibited no correlations (p > 0.05) with cephalometric parameters. However, it was significantly correlated (p < 0.01) with the mandibular retromolar bone volume. No gender-specific differences (p > 0.05) were observed concerning bone volumes in all bone harvesting regions. Nevertheless, the male population's interforaminal distance in the chin region was significantly higher (p = 0.001). CONCLUSIONS: PRs and LCRs can be used at the initial stage of peri-implant augmentation planning to deduce conclusions about the bone volume in different intraoral bone donor sites. It can help describe indications and justify additional diagnostic options, such as three-dimensional radiologic techniques.

Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs.

OBJECTIVE: In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs. STUDY DESIGN: YOLO V4, a CNN (Convolutional Neural Networks) based object detection model was used for automated tooth detection and numbering. 4545 pediatric panoramic X-ray images, processed in labelImg, were trained and tested in the Yolo algorithm. RESULTS AND CONCLUSIONS: The model was successful in detecting and numbering both primary and permanent teeth on pediatric panoramic radiographs with the mean average precision (mAP) value of 92.22 %, mean average recall (mAR) value of 94.44% and weighted-F1 score of 0.91. The proposed CNN method yielded high and fast performance for automated tooth detection and numbering on pediatric panoramic radiographs. Automatic tooth detection could help dental practitioners to save time and also use it as a pre-processing tool for detection of dental pathologies.