Cleidocranial dysplasia (CCD) is a rare, congenital disorder characterized by a unique constellation of skeletal and dental abnormalities. The imaging findings, combined with clinical examination, help establish a definitive diagnosis. Understanding the broad spectrum of manifestations in CCD is essential for effective management and treatment. This case report aims to provide a comprehensive overview of a 25-year-old male patient with CCD, highlighting the genetic etiologies, clinical presentation, radiological findings, and a review of current literature to enhance understanding and awareness of this rare condition.

BACKGROUND: This study aimed to develop a new formula to easily estimate the vertical dimension of occlusion (VDO) by using the distance between the mental foramen on a panoramic radiograph.
METHODS: A total of 508 dentulous subjects were selected from outpatient dental clinics at the College of Dental Medicine, Al-Azhar University. The vertical dimension of the occlusion was measured using a single calibrated calliper. For each subject, a digital panoramic radiograph was taken with fixed exposure parameters. The intermental foramina distance (IMFD) was measured. The data were collected and then analysed using the IBM SPSS version 20.0 software package. (Armonk, NY: IBM Corp.). Linear regression was used to determine the relationship between the intermental foramina distance (IMFD) and the vertical dimension at occlusion (VDO).
RESULTS: Pearson\'s correlation analysis revealed that there was a strong correlation between the intermental foramina distance (IMFD) and the VDO. Thus, a novel formula was developed for determining the VDO using panoramic radiography.
CONCLUSIONS: The novel formula developed herein facilitated the determination of the VDO among prosthetic rehabilitation for subjects who lost vertical dimension due to loss of posterior teeth or severe wear of natural posterior teeth. Further studies are needed to determine the clinical applicability of the derived formulae for edentulous subjects.

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.

BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3.
METHODS: The study included 1367 LM3 images from 784 patients who presented from 2021-2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3.
RESULTS: The ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels.
CONCLUSIONS: The development of a 3-phase computer-aided visualisation-based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.

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UNASSIGNED: The canine plays a vital role in dentofacial aesthetics and function. It supports the base of the alar and upper lip, which are crucial for smile aesthetics. When impacted, these functions are lost, leading to low self-esteem and overall poor health-related quality of life. The present study was conducted to find the prevalence of impacted and transmigrated canines in orthodontic patients and also to find the most prevalent type of canine impaction.
UNASSIGNED: This retrospective study was conducted in a hospital setting at Dental College. A total of 3050 OPGs (Orthopantomagram) of patients who visited dental hospitals for orthodontic treatment constituted the final sample. Demographic details regarding age, gender, and place of residence were collected from the patients. Evaluation of sample radiographs on the standard light box was performed to collect data regarding impacted and transmigrated canines. Statistical analysis was performed using descriptive statistics and Chi-square test.
UNASSIGNED: Prevalence of impacted canine was found to be 2.46%. Impacted canine prevalence of 1.53% and 2.85% was reported in males and females, respectively. Only two female patients had transmigrated mandibular impacted canines. Comparison of arch showed a statistically significant (P value 0.02) higher prevalence in the maxillary arch, which was 1.54%, and in the mandibular arch, it was 0.92%. The present study reported significantly more unilateral impactions (P value 0.00) than bilateral impactions.
UNASSIGNED: The overall prevalence for impacted canine was 2.46%. Prevalence was higher in female patients. Early diagnosis of impacted canines is vital for planning orthodontic treatment in such patients.

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.

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12-70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment.

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

OBJECTIVE: To investigate the prevalence of tooth agenesis and associated dental anomalies in Latvian adolescent dental patients and compare it to other European countries.
METHODS: Cross-sectional study of 2692 11-to-14-year-old patients (39.9% males and 60.1% females) attending Riga Stradins University Institute of Stomatology with panoramic radiographs taken between August 2020 and September 2021. Patients with any genetic syndromes were excluded. Data on tooth agenesis (excluding third molars) and other dental anomalies were recorded.
RESULTS: The prevalence of tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant difference between genders (χ2 test, p = 0.472). The most commonly missing teeth were mandibular second premolars, followed by upper lateral incisors and upper second premolars. There was a statistically significant association with the presence of other dental anomalies in tooth agenesis patients (p < 0.001).
CONCLUSIONS: This study found that the prevalence of non-syndromic tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant differences between the genders. Patients with tooth agenesis have a statistically significant possibility of the presence of other dental anomalies (p < 0.001).