Accuracy of different dental age assessment methods to determine chronological age among children with special needs.

AIM: This study aimed to validate the accuracy of dental age (DA) based on the dental development of permanent teeth in children with special needs using Demirjian, Willems, and London Atlas methods and to correlate the dental and chronological age (CA) of children with special needs in Malaysia. MATERIALS AND METHODS: The panoramic radiographic images belonging to children with special needs from the two teaching dental hospitals in Malaysia aged between 5 and 16 years were included in the study. The evaluation was performed by two observers using three methods (London Atlas, Demirjian, and Willems methods) to estimate the accurate DA. The outcome was determined by comparing the mean of the DA and CA. RESULTS: A total of 52 panoramic radiographs were available for the analysis. The London Atlas and Demirjian methods overestimated the DA with a mean of 0.05 and 0.20 years, respectively, while the Willems method underestimated by 0.19 years. The London Atlas method was highly precise and accurate, while Demirjian and Willems methods were the least precise and accurate. CONCLUSION: The London Atlas method of DA estimation is highly accurate and valid for children with special needs in the Malaysian population, followed by the Willems and Demirjian methods.

Endoscopically-assisted extraction of broken roots or fragments within the mandibular canal: a retrospective case series study.

PURPOSE: To assess the impact of endoscope-assisted fractured roots or fragments extraction within the mandibular canal, along with quantitative sensory testing (QST) alterations in the inferior alveolar nerve (IAN). METHODS: Six patients with lower lip numbness following mandibular third molar extraction were selected. All patients had broken roots or fragments within the mandibular canal that were extracted under real-time endoscopic assistance. Follow-up assessments were conducted on postoperative days 1, 7, and 35, including a standardized QST of the lower lip skin. RESULTS: The average surgical duration was 32.5 min, with the IAN exposed in all cases. Two of the patient exhibited complete recovery of lower lip numbness, three experienced symptom improvement, and one patient remained unaffected 35 days after the surgery. Preoperative QST results showed that the mechanical detection and pain thresholds on the affected side were significantly higher than those on the healthy side, but improved significantly by postoperative day 7 in five patients, and returned to baseline in two patients on day 35. There were no significant differences in the remaining QST parameters. CONCLUSIONS: All endoscopic surgical procedures were successfully completed without any additional postoperative complications. There were no cases of deterioration of IAN injury, and lower lip numbness recovered in the majority of cases. Endoscopy allowed direct visualization and examination of the affected nerve, facilitating a comprehensive analysis of the IAN.

Graphical user interface-based convolutional neural network models for detecting nasopalatine duct cysts using panoramic radiography.

Nasopalatine duct cysts are difficult to detect on panoramic radiographs due to obstructive shadows and are often overlooked. Therefore, sensitive detection using panoramic radiography is clinically important. This study aimed to create a trained model to detect nasopalatine duct cysts from panoramic radiographs in a graphical user interface-based environment. This study was conducted on panoramic radiographs and CT images of 115 patients with nasopalatine duct cysts. As controls, 230 age- and sex-matched patients without cysts were selected from the same database. The 345 pre-processed panoramic radiographs were divided into 216 training data sets, 54 validation data sets, and 75 test data sets. Deep learning was performed for 400 epochs using pretrained-LeNet and pretrained-VGG16 as the convolutional neural networks to classify the cysts. The deep learning system's accuracy, sensitivity, and specificity using LeNet and VGG16 were calculated. LeNet and VGG16 showed an accuracy rate of 85.3% and 88.0%, respectively. A simple deep learning method using a graphical user interface-based Windows machine was able to create a trained model to detect nasopalatine duct cysts from panoramic radiographs, and may be used to prevent such cysts being overlooked during imaging.

Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

AIM: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. CONCLUSION: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

Isolated dentinogenesis imperfecta: Novel DSPP variants and insights on genetic counselling.

OBJECTIVE: Dentinogenesis imperfecta (DI) is an inherited dentin defect and may be isolated or associated with disorders such as osteogenesis imperfecta, odontochondrodysplasia Ehler-Danlos and others. Isolated DI is caused mainly by pathogenic variants in DSPP gene and around 50 different variants have been described in this gene. Herein, we report on 19 patients from two unrelated Egyptian families with isolated DI. Additionally, we focused on genetic counselling of the two families. MATERIALS AND METHODS: The patients were examined clinically and dentally. Panoramic X-rays were done to some patients. Whole exome sequencing (WES) and Sanger sequencing were used. RESULTS: WES revealed two new nonsense variants in DSPP gene, c.288T > A (p.Tyr96Ter) and c.255G > A (p.Trp85Ter). Segregation analysis by Sanger sequencing confirmed the presence of the first variant in all affected members of Family 1 while the second variant was confirmed to be de novo in the patient of Family 2. CONCLUSIONS AND CLINICAL RELEVANCE: Our study extends the number of DSPP pathogenic variants and strengthens the fact that DSPP is the most common DI causative gene irrespective of patients' ethnicity. In addition, we provide insights on genetic counseling issues in patients with inherited DSPP variants taking into consideration the variable religion, culture and laws in our society.

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

BACKGROUND: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS: The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS: These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

A comparison of estimated age based on pulp volume from cone beam computed tomography (CT) images and panoramic radiography data with chronological age.

This retrospective study was conducted to evaluate different methods for dental age estimation in children and to examine the feasibility of using cone beam computed tomography (CBCT) data for age estimation. A total of 200 radiographic records (both digital panoramic radiographs and CBCTs) were acquired from 100 children aged 9 to 16 years, all taken on the same dates. Radiographic data was acquired from archived records and included both panoramic radiography and CBCT data belonging to the same individual. CBCT was used when panoramic radiographic data was insufficient. The pulp volume and pulp/tooth volume ratio of the left first molar teeth in the mandible were calculated from the CBCT data using MIMICS software. In addition, age was estimated by the Demirjian and Willems methods from data obtained from panoramic radiography images. Statistical analyses and linear regression analysis were performed as necessary. There was a statistically significant difference between the mean difference between the Demirjian method and chronological age, and between the Willems method and chronological age (p < 0.001). Statistically significance was achieved in a linear regression model created from pulp volume (R2 = 0.098) and pulp/tooth volume ratio (R2 = 0.395) data for the estimated dental age analysis (p < 0.001) and a negative correlation was observed with chronological age. When compared estimated dental age from CBCT data with chronological age, the pulp/tooth volume ratio method yielded results closer to chronological age than using only pulp volume data. When considering both panoramic radiographic age estimation methods and age estimation methods using CBCT data, we found that the results obtained with the Willems method, a panoramic radiographic age estimation technique, provided the closest results to the chronological age. More contributions should be made to the literature regarding the feasibility of age estimation using pulp and tooth volume as an alternative method.

The utilization of decision trees on orthopantomographic and lateral panoramic graphs for the diagnosis of unilateral anterior disc displacement of the temporomandibular joint.

BACKGROUND: Investigation is to utilize decision trees in conjunction with orthopantomography (OPT) and lateral panoramic graphy (LPG) to diagnose unilateral anterior disc displacement (ADD) of the temporomandibular joint. METHODS: In this study, 161 patients with images obtained through all three imaging methods, MRI, OPT, and LPG, were selected from the archives. The participants were categorized into two groups: the study group, comprising 89 patients with unilateral anterior disc displacement, and the control group, consisting of 72 healthy individuals. Measurements, including 2 angles (antero-posterior angle and superior-inferior angle) and 3 distance parameters (anterior joint space distance, superior joint space distance, and posterior joint space distance), were conducted on each imaging modality dataset. To assess the obtained measurement data within each patient, the differences from each measurement were calculated. Statistical analysis of the measurement differences between the control and study groups was carried out with independent t test, and decision trees were generated using the SPSS 25 decision tree module 5.0. RESULTS: In ADD patients, it was statistically significantly found that the APA increased while the SIA decreased for angle measurements. But for linear measurements, AS increased while the SS and PS decreased in MRI, OPT, and LPG. CONCLUSION: ADD can be diagnosed in OPT and LPG. The identification of the specific type of ADD that occurs in the temporomandibular joint is not feasible.

Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.

OBJECTIVES: To evaluate the performance of a commercially available Generative Pre-trained Transformer (GPT) in describing and establishing differential diagnoses for radiolucent lesions in panoramic radiographs. MATERIALS AND METHODS: Twenty-eight panoramic radiographs, each containing a single radiolucent lesion, were evaluated in consensus by three examiners and a commercially available ChatGPT-3.5 model. They provided descriptions regarding internal structure (radiodensity, loculation), periphery (margin type, cortication), shape, location (bone, side, region, teeth/structures), and effects on adjacent structures (effect, adjacent structure). Diagnostic impressions related to origin, behavior, and nature were also provided. The GPT program was additionally prompted to provide differential diagnoses. Keywords used by the GPT program were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean score values and standard deviation were calculated for each description. Performance in establishing differential diagnoses was assessed using Rank-1, -2, and - 3. RESULTS: Descriptions of margination, affected bone, and origin received the highest scores: 0.93, 0.93, and 0.87, respectively. Shape, region, teeth/structures, effect, affected region, and nature received considerably lower scores ranging from 0.22 to 0.50. Rank-1, -2, and - 3 demonstrated accuracy in 25%, 57.14%, and 67.85% of cases, respectively. CONCLUSION: The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application. CLINICAL RELEVANCE: Understanding the potential role of GPT systems as an auxiliary tool in image interpretation is imperative to validate their clinical applicability.

The use of panoramic radiographs to address the oral health needs of vulnerable Canadian populations.

Background: The high demand for oral health care services among vulnerable individuals combined with limited available resources requires a rethinking of the provision of oral health services. This study aimed to determine the usefulness of the dental panoramic radiograph (DPR) as an imaging tool to assess oral health and prioritize dental interventions in vulnerable patients. Methods: This observational and retrospective study evaluated charts of patients who attended Public Health Dental Clinics (PHDC), Alberta Health Services (AHS), in Calgary, Canada, between January 2018 and December 2019. Data collected included sex and age at the time of image acquisition. The following radiographic findings were gathered: the number of missing, present, decayed, restored, and impacted teeth; periapical lesions; retained root; periodontal bone loss; odontogenic and non-odontogenic lesions in the jaws; carotid calcification; and incidental radiographic findings with clinical relevance. Results: Of the 526 DPRs evaluated, 57.4% were from male patients and 42.6% were from female patients, with a mean age of 38.5 years. The average number of present teeth in females and males was 23.7 and 22.6, respectively. The most prevalent dental-related finding was periodontal bone loss (81.5%), followed by periapical lesions (59.6%) and impacted teeth (27%). Among non-dental findings, osseous lesions of the jaws were found in 10.4% of the patients, and carotid atheroma had a frequency of 3.2%. Conclusion: The DPR is a useful adjunct to the clinical exam in this specific population. It provides an opportunistic overview of their oral health and necessary support to establish priorities in oral health care in a public health setting.

Contexte : La forte demande de services de santé buccodentaire chez les personnes vulnérables, combinée aux ressources limitées disponibles, nécessite de repenser la prestation des services de santé buccodentaire. Cette étude vise à déterminer l'utilité de la radiographie panoramique comme outil d'imagerie pour évaluer la santé buccodentaire et prioriser les interventions dentaires chez les patients vulnérables. Méthodes : Cette étude observationnelle et rétrospective a évalué les dossiers des patients qui ont fréquenté les cliniques dentaires de santé publique d'Alberta Health Services (AHS) à Calgary, au Canada, entre janvier 2018 et décembre 2019. Les données recueillies comprenaient le sexe et l'âge au moment de l'acquisition de l'image. Les résultats radiographiques suivants ont été recueillis : nombre de dents manquantes, présentes, cariées, réparées et incluses; lésions périapicales; racine résiduelle; perte osseuse parodontale; lésions odontogéniques et non odontogéniques dans les mâchoires; calcification carotidienne; et résultats radiographiques accessoires pertinents sur le plan clinique. Résultats : Parmi les 526 radiographies panoramiques évaluées, 57,4 % provenaient d'hommes et 42,6 % de femmes, avec un âge moyen de 38,5 ans. Le nombre moyen de dents présentes chez les femmes et les hommes était de 23,7 et 22,6, respectivement. La découverte la plus courante liée aux soins dentaires était la perte osseuse parodontale (81,5 %), suivie des lésions périapicales (59,6 %) et des dents incluses (27 %). En ce qui concerne les résultats non liés aux soins dentaires, des lésions osseuses des mâchoires ont été repérées chez 10,4 % des patients, et l'athérome carotidien avait une fréquence de 3,2 %. Conclusion : La radiographie panoramique est un complément utile à l'examen clinique dans cette population particulière. Elle donne un aperçu de leur santé buccodentaire et le soutien nécessaire pour établir les priorités en matière de soins buccodentaires dans un contexte de santé publique.

Deep learning methods for fully automated dental age estimation on orthopantomograms.

OBJECTIVES: This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment. METHODS: A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset. RESULTS: The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input. CONCLUSIONS: The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. CLINICAL RELEVANCE: The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.

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.

Investigation of the relationship between the mandibular third molar teeth and the inferior alveolar nerve using posteroanterior radiographs: a pilot study.

BACKGROUND: The most severe complication that can occur after mandibular third molar (MM3) surgery is inferior alveolar nerve (IAN) damage. It is crucial to have a comprehensive radiographic evaluation to reduce the possibility of nerve damage. The objective of this study is to assess the diagnostic accuracy of panoramic radiographs (PR) and posteroanterior (PA) radiographs in identifying the association between impacted MM3 roots and IAN. METHODS: This study included individuals who had PR, PA radiographs, and cone beam computed tomography (CBCT) and who had at least one impacted MM3. A total of 141 impacted MM3s were evaluated on CBCT images, and the findings were considered gold standard. The relationship between impacted MM3 roots and IAN was also evaluated on PR and PA radiographies. The data was analyzed using the McNemar and Chi-squared tests. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PR and PA radiographies were determined. RESULTS: Considering CBCT the gold standard, the relationship between MM3 roots and IAN was found to be statistically significant between PR and CBCT (p = 0.00). However, there was no statistically significant relationship between PA radiography and CBCT (0.227). The study revealed that the most prevalent limitation of the PR in assessing the relationship between MM3 roots and IAN was the identification of false-positive relationship. CONCLUSIONS: PA radiography may be a good alternative in developing countries to find out if there is a contact between MM3 roots and IAN because it is easier to get to, cheaper, and uses less radiation.

Correlation between dental and skeletal maturity in Korean children based on dental maturity percentile: a retrospective study.

BACKGROUND: The correlation between dental maturity and skeletal maturity has been proposed, but its clinical application remains challenging. Moreover, the varying correlations observed in different studies indicate the necessity for research tailored to specific populations. AIM: To compare skeletal maturity in Korean children with advanced and delayed dental maturity using dental maturity percentile. DESIGN: Dental panoramic radiographs and cephalometric radiographs were obtained from 5133 and 395 healthy Korean children aged between 4 and 16 years old. Dental maturity was assessed with Demirjian's method, while skeletal maturity was assessed with the cervical vertebral maturation method. Standard percentile curves were developed through quantile regression. Advanced (93 boys and 110 girls) and delayed (92 boys and 100 girls) dental maturity groups were defined by the 50th percentile. RESULTS: The advanced group showed earlier skeletal maturity in multiple cervical stages (CS) in both boys (CS 1, 2, 3, 4, and 6) and girls (CS 1, 3, 4, 5, and 6). Significant differences, as determined by Mann-Whitney U tests, were observed in CS 1 for boys (p = 0.004) and in CS 4 for girls (p = 0.037). High Spearman correlation coefficients between dental maturity and cervical vertebral maturity exceeded 0.826 (p = 0.000) in all groups. CONCLUSION: A correlation between dental and skeletal maturity, as well as advanced skeletal maturity in the advanced dental maturity group, was observed. Using percentile curves to determine dental maturity may aid in assessing skeletal maturity, with potential applications in orthodontic diagnosis and treatment planning.

Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs.

Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.

Application of panoramic radiographs in the diagnosis of temporomandibular disorders.

To evaluate the feasibility of temporomandibular disorder (TMD) diagnosis with panoramic radiography, and provide standardized data for artificial intelligence-assisted diagnosis by measuring the differences in the condylar and mandibular ramus heights. A total of 500 panoramic radiographs (219 male and 281 female participants) of healthy individuals were examined. The panoramic machine compatible measurement software, SCANORA 5.2.6, was used to measure the bilateral condylar height and mandibular ramus height, and SPSS 27.0 was used to calculate the left- and right-side differences in condylar height and mandibular ramus height of healthy individuals. Magnetic resonance images of the temporomandibular joint region obtained from 46 outpatients in the Stomatology Department were selected along with their corresponding panoramic radiographs. The left- and right-sided differences were measured and compared with the magnetic resonance imaging results. The measurement data are expressed as mean ±â€…standard deviation (mm). t Tests were used to analyze data from healthy male and healthy female groups. The findings revealed that while there was no significant difference (P > .05) in the height of the condyle between men and women, there was a significant difference (P  < .05) in the height of the mandibular ramus. In healthy population, the difference in height between the left and right condyle was 1.09 ±â€…0.99 mm. The difference in height of mandibular ramus in men was 1.26 ±â€…0.85 mm and that in women was 1.19 ±â€…0.87 mm. For the diagnosis of TMD, the sensitivity of panoramic radiographs was 94.74% (36/38), specificity was 75.00% (6/8), and diagnostic accuracy was 91.30% (42/46). The height of the right and left lateral condyles was not identical in healthy individuals, resulting in a discernible height discrepancy. In addition, the height of the mandibular ramus varied. By considering the left-right lateral height differences identified in this study along with clinical examination, it is possible to employ this metric as a preliminary screening tool for patients with TMD. Further, the use of panoramic radiographs for initial TMD screening is both viable and significant.

Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.

OBJECTIVES: Automating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a first step. METHODS: A convolutional neural network with SqueezeNet architecture was first trained to classify two groups of PRs (91with and 91without impacted canines) on the MATLAB programming platform. Based on results, the need to crop the PRs was realized. Next, artificial intelligence (AI) detectors were trained to identify specific landmarks (maxillary central incisors, lateral incisors, canines, bicuspids, nasal area, and the mandibular ramus) on the PRs. Landmarks were then explored to guide cropping of the PRs. Finally, improvements in classification of automatically cropped PRs were studied. RESULTS: Without cropping, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for classifying impacted and nonimpacted canine was 84%. Landmark training showed that detectors could correctly identify upper central incisors and the ramus in ∼98% of PRs. The combined use of the mandibular ramus and maxillary central incisors as guides for cropping yielded the best results (∼10% incorrect cropping). When automatically cropped PRs were used, the AUC-ROC improved to 96%. CONCLUSIONS: AI algorithms can be automated to preprocess PRs and improve the identification of impacted canines.

Exploring the association between PITX2, third molars agenesis and sella turcica morphology : PITX2, third molars agenesis and sella turcica morphology.

OBJECTIVES: PITX2 is required for mammalian development and single nucleotide polymorphisms (SNPs) in this gene could be involved in dental agenesis and sella turcica patterns. Thus, the present study evaluated the association between SNPs in PITX2, third molars agenesis and sella turcica phenotypes. MATERIALS AND METHODS: The sample consisted of healthy orthodontic German patients with lateral cephalometric radiographs with clearly visualization of the sella turcica, and dental orthopantomograms. The morphological variations of the sella turcica were evaluated using the lateral cephalograms, while third molar agenesis was evaluated using orthopantomograms. DNA isolated from buccal cells was used for genotyping three SNPs in PITX2 (rs3796902, rs1947187, and rs2595110). The analyzes were performed using a significance of 5%. There was no association between third molar agenesis and sella turcica phenotypes (p > 0.05). SNPs in PITX2 were also not associated with third molars agenesis (p > 0.05). RESULTS: SNPs in PITX2 were associated with sella turcica phenotypes. The rs3796902 was associated with hypertrophic posterior clinoid process (p = 0.013). The rs1947187 and rs2595110 were associated with sella turcica bridge type A (p = 0.013 and p = 0.011, respectively for genotype distribution). Patients that carry the genotypes GG-CC-AG (rs3796902- rs1947187- rs2595110) had 7.2 higher chance to present sella turcica bridge type A (p = 0.002; Odds ratio = 7.2, Confidence interval 95% 2.04-27.04). CONCLUSIONS: Third molar agenesis was not associated with SNPs in PITX2 and sella turcica phenotypes. SNPs in PITX2 may have an important role in sella turcica pattern.

Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort.

Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data. The dataset includes 716 panoramic radiograph data for jaw bone lesions and normal cases collected and labeled by two radiologists from January 2019 to February 2021. Experiments show that guiding network attention with even 5% of attention-labeled data can enhance the diagnostic accuracy for pathology from 92.41 to 96.57%. Furthermore, ablation studies reveal that the proposed augmentation methods outperform prior preprocessing and augmentation combinations, achieving an accuracy of 99.17%. The results affirm the capability of the proposed framework in fine-grained diagnosis using minimally labeled data, offering a practical solution to the challenges of medical image analysis.

Evaluation of Jaw Bone Change in Children and Adolescents with Rheumatic Heart Disease by Fractal Analysis.

BACKGROUND: Recognizing the oral needs of people living with rheumatic heart disease (RHD), particularly in relation to their jaws, is of paramount importance. AIMS: This study aimed to evaluate potential jaw bone changes in children and adolescents diagnosed with RHD and compare them with a group of healthy children and adolescents. METHODS AND MATERIAL: The dental panoramic radiographs (DPRs) of 32 children and adolescents diagnosed with RHD comprised the case group. In contrast, the control group consisted of DPRs from 32 children and adolescents without any known diseases. Two regions of interest (ROI1 and ROI2) of trabecular bone and a separate region of interest (ROI3) of cortical bone were selected for fractal analysis. The mandibular cortical index (MCI) was evaluated. RESULTS: The ROI1 fractal dimension (FD) value of the individuals in the case group was statistically higher in the right jaw than in the left jaw (P < 0.01). Significant differences were determined between the case and control groups regarding the left ROI1, and the left and the right ROI2 (P < 0.05). No significant difference was found between the patient and control groups regarding the MCI value (P > 0.05). CONCLUSIONS: The results of this study showed that bone metabolism was affected unilaterally in the condyle region and bilaterally in the gonial region of patients with RHD. It also revealed that the cortical layer was not affected.