Panoramic Radiographic Images Research Articles

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to preserve vital pulp. This study aimed to develop a deep learning model to assist dentists in early diagnosing dens evaginatus, thereby supporting early intervention and mitigating the risk of severe consequences. In this study, a deep learning model was developed utilizing panoramic radiograph images sourced from 1410 patients aged 3-16 years, with high-quality annotations to enable the automatic detection of dens evaginatus. Model performance and model's efficacy in aiding dentists were evaluated. The findings indicated that the current deep learning model demonstrated commendable sensitivity (0.8600) and specificity (0.9200), outperforming dentists in detecting dens evaginatus with an F1-score of 0.8866 compared to their average F1-score of 0.8780, indicating that the model could detect dens evaginatus with greater precision. Furthermore, with its support, young dentists heightened their focus on dens evaginatus in tooth germs and achieved improved diagnostic accuracy. Based on these results, the integration of deep learning for dens evaginatus detection holds significance and can augment dentists' proficiency in identifying such anomaly.

Fractal dimension, lacunarity, and bone area fraction analysis of peri-implant trabecular bone after prosthodontic loading.

To assess the structural alterations in peri-implant bone occurring 5years after prosthodontic loading in panoramic radiography (PR). PR images of 44 mandibular and 33 maxillary implants along with 42 healthy control teeth taken before and 5years after prosthodontic loading were included. Two regions-of-interest (ROI) were selected from mesial and distal surrounding bone of each implant and tooth. Then, the selected ROIs were divided to obtain three sub-ROIs (coronal, middle, and apical) on each side. A total of eight ROIs and sub-ROIs from each implant and control tooth were used for the calculations of fractal dimension (FD), lacunarity, and bone area fraction (BA/TA). The paired-sample t test was used to compare measurements before and 5years after loading (p = 0.05). Overall evaluation of 77 implants showed that FD decreased at the middle and apical peri-implant bone levels 5years after loading (p < 0.05). In mandibular implants, BA/TA decreased after loading (p < 0.05). While FD decreased at the coronal level (p = 0.022), lacunarity increased at the middle level of mandibular implants (p < 0.05). In maxillary implants, FD decreased at the middle and BA/TA decreased at the coronal level (p < 0.05). On the other hand, BA/TA increased at the apical level of maxillary implants (p = 0.016) after loading. None of the parameters revealed any difference in the control group (p > 0.05). FD and BA/TA can be used to analyze structural changes in peri-implant bone after prosthodontic loading. Additionally, FD, lacunarity and BA/TA may provide useful information about changes occurring at different levels of peri-implant bone.

Style harmonization of panoramic radiography using deep learning.

This study aimed to harmonize panoramic radiograph images from different equipment in a single institution to display similar styles. A total of 15,624 panoramic images were acquired using two different equipment: 8079 images from Rayscan Alpha Plus (R-unit) and 7545 images from Pax-i plus (P-unit). Among these, 222 image pairs (444 images) from the same patients comprised the test dataset to harmonize the P-unit images with the R-unit image style using CycleGAN. Objective evaluations included Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) assessments. Additionally, expert evaluation was conducted by two oral and maxillofacial radiologists on transformed P-unit and R-unit images. The statistical analysis of LPIPS employed a Student's t-test. The FID and mean LPIPS values of the transformed P-unit images (7.362, 0.488) were lower than those of the original P-unit images (8.380, 0.519), with a significant difference in LPIPS (p < 0.05). The experts evaluated 43.3-46.7% of the transformed P-unit images as R-unit images, 20.0-28.3% as P-units, and 28.3-33.3% as undetermined images. CycleGAN has the potential to harmonize panoramic radiograph image styles. Enhancement of the model is anticipated for the application of images produced by additional units.

Dentin thickness model: A novel method for dental age estimation using panoramic radiography

The study aims to evaluate the reliability of the novel dentin thickness model based on root parameters when applied to panoramic radiography images of mandibular first molar in the southern Turkish subpopulation. A total of 946 panoramic images were analyzed for the study. The samples were allocated into four age groups: Group 1(between 10 and 18), Group 2(between 19 and 30), Group 3(between 31 and 50), Group 4(between 51 and 78). The dentin thickness model measurements consisted of root dentin thickness of the mesial aspect of the distal root and distal canal width. Regression, intraclass correlation coefficient, and Durbin-Watson analyses were used for statistics. A medium correlation was detected in the dentin thickness model (R = 0.398). Root dentin thickness and canal width had a significant effect (p < 0.05), but gender did not (p > 0.05). Group 3 showed the highest accuracy (p < 0.001). Group 4 presented the lowest correlation with chronological age compared to other age groups (p < 0.001). The dentin thickness model showed a promising result for the dental age estimation with a medium correlation. The dentin thickness model included root measurements of the mesial aspect of the mandibular first molar without any crown parameters in the southern Turkish subpopulation. Gender did not affect the dentin thickness model.

Comparative evaulation of condylar guidance obtained by digital panoramic radiographic images with that of obtained during jaw relation and try-in using two different interocclusal record material in completely edentulous patients: an in vivo study

Background In completely edentulous patients, maintaining the patient’s temporomandibular joint (TMJ) health and intraoral components is very essential, which can be achieved by an accurate interocclusal record, which can be taken by various materials and also at various stages. Simulation of the temporomandibular joint anatomy and mandibular movements is possible on a semi-adjustable articulator, which is widely used in prosthodontics. Transferring the record on articulator is a critical step in the fabrication of complete dentures, so it should be done precisely to reduce chair side time and also to avoid complications for patients. This study examines which material is more accurate and records which stage gives more precision by comparing the Condylar guidance on radiographs. This is because it is a relatively consistent bony landmark and can be used to correlate with clinical methods in terms of verification. The objective is to determine the inter occlusal records created during the Jaw Relation stage and Try-in using reinforced wax and Polyvinyl siloxane and compare these values to the condylar guidance obtained by orthopantomogram (OPG). Methods Recording the Condylar Guidance value on OPG by tracing the Glenoid fossa, Articular eminence, and the Frankfurt Horizontal Plane. Followed by conventional steps in making complete dentures at jaw relation and try-in stage. We will record values of condylar guidance with the two different Interocclusal record materials. Expected result It will help the clinicians to gain knowledge about obtaining precise condylar inclination angle by using suitable interocclusal record materials. Conclusions This study will help us to obtain condylar inclination, which in turn helps the patient’s satisfaction as it preserves the intraoral components and health of TMJ.

Comparative Evaluation of Condylar Guidance Obtained by Digital Panoramic Radiographic Images Using Two Different Interocclusal Recording Material During Jaw Relation in Completely Edentulous Patients: An In Vivo Study.

Objective To compare the condylar guidance value obtained during jaw relation by two different interocclusal recording materials to that obtained from OPG (Orthopantomogram). Materials and methods Two different interocclusal recording materials were used, namely bite registration wax (reinforced wax, by MAARC Dental Company) and bite registration paste (jet bite, coltene) for interocclusal records during jaw relations. An OPG is shot prior to the treatment, and horizontal condylar guidance (HCG) is traced joining the bony landmarks in a completely edentulous patient seeking complete denture treatment. All the conventional steps for complete denture fabrication up to jaw relation are performed. A facebow transfer is done on a Hanau H2 articulator, and height tracers (extra oral tracing devices) are attached. After satisfactory training, the tracing is performed by the patient, and the interocclusal records are obtained. The articulator is programmed to obtain the value of HCG using protrusive records, which are then compared to the value obtained from OPG. Statistics were carried out to determine angles. Followed by statistical analysis with Pearson correlation to analyze the results (α = 0.01). Results A substantial relationship was discovered between the horizontal condylar inclination between two interocclusal recording materials and the corresponding OPG for both the right (p = 0.0015) and left (p = 0.0007) sides. Conclusion The condylar guidance obtained with jet bite was closer than the condylar guidance value obtained with reinforced wax when compared with the horizontal condylar inclination obtained in OPG by tracing with a mean difference of 10° for polyvinyl siloxane and 20° for reinforced wax in seven subjects evaluated.

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as (H); enamel-dentin caries lesions treated with restorative filling, coded as (R); and deep dental caries that underwent root canal treatment, coded as (E). The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.

Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.

Comparative analysis of clinical image evaluation charts for panoramic radiography

ObjectiveTo compare and analyze professional (P chart) and simple (S chart) clinical image evaluation charts for evaluating panoramic radiograph image quality.MethodsTen evaluators assessed 285 clinical panoramic radiograph images. The evaluators were divided into oral and maxillofacial radiologists (OMFR, n = 5) and general dentist (dentists not specializing in oral and maxillofacial radiology, G, n = 5) groups. For image evaluation, P and S charts provided by the Korean Academy of Oral and Maxillofacial Radiology were used. Scores of items for each evaluation chart were used to compare the reliability, correlation, evaluation scores, evaluation time, and preference, and statistical analyses were performed using IBM SPSS Statistics.ResultsThe S chart showed similar levels of evaluation scores at shorter evaluation time, as compared to the P chart. In the results for each evaluation chart, all analyzed correlations were statistically significant. Total score, image density/contrast/sharpness, and overall image quality items showed a very high positive correlation in the P chart. While the overall range of correlation coefficients was relatively lower in the S chart than the P chart, the same items showed high correlation coefficients. In the preference evaluation, both the professional and generalist groups preferred the S chart.ConclusionsA comparative analysis with the P chart, revisions, and upgrades are needed for the S chart items that showed low correlations in this study, such as artifacts, coverage area, and patient movement.

Idiopathic Osteosclerosis among UiTM Dental Centre Patients: A Retrospective Study

Idiopathic osteosclerosis (IO) represents asymptomatic radiopaque masses within the jaw. IO is commonly found as an incidental finding in panoramic radiographs. The purpose of the present study is to determine the prevalence of IO among Universiti Teknologi MARA (UiTM) Dental Centre patients and to investigate the radiographic parameters of IO located in the maxilla-mandibular region. Panoramic radiographic images of patients attending UiTM Dental Centre from 2016 to 2020 were examined. Parameters such as gender, age, site, shape, size, and association with tooth were evaluated. The data obtained were analysed using SPSS version 27. Pearson chi-square test was used to evaluate the relationship between the parameters. IO was identified in 108 (9.67%) out of 1,117 panoramic radiographic images that were reviewed. There was a slightly higher prevalence of IO in female patients with 58 (53%) over male patients. Most of the radiographically identified IO were located in the posterior mandibular region, constituting 83% of the cases. Pearson chi-square test revealed a significant relationship between size and gender (p-value &lt; 0.05). Significant associations were found between site and gender and association with tooth and foci (p-value &lt; 0.01). The prevalence of IO among UiTM Dental Center patients was observed to be low at 9.67%. There was a higher occurrence of IO in female patients compared to males, and the posterior mandibular region emerged as the most common site where IO was radiographically identified. The accurate radiographic identification of IO is essential for providing a precise diagnosis and formulating an appropriate treatment plan.

Perbedaan Tingkat Keakuratan Estiemasi Usia Menggunakan Metode Al-Qahtani Dan Metode Cameriere Pada Gambaran Radiografi Panoramik

Radiography can be used at prenatal age because at this age we can see and assess the stage of formation and development of primary teeth that have not yet erupted. The London Atlas method is well-organized, comprehensive, and evidence-based using tooth development and alveolar eruption. The method for determining chronological age based on open apex measurements was first introduced by Roberto Cameriere. This method looks at the development of the seven permanent teeth of the lower left jaw. Research objective: To determine the difference in the level of accuracy of age estimation using the Al-Qahtani method and the Cameriere method on panoramic radiography images. Materials and Methods: This research design uses analytical observational with a cross sectional research design. The statistical test used is the paired t-test. Results: Based on the results of this study, it is known that the difference in average age in the Al-Qahtani method is that the tooth age is 0.6 years older than the chronological age and in the Cameriere method the tooth age is 0.59 years older than the chronological age. Conclusion: The results of this study show that there are not differences in age estimation using the Al-Qahtani method and the Cameriere method on panoramic radiography images.

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT.

To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam CT (CBCT). PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions-of-interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between the MRONJ and control groups for both imaging modalities (P = .05). MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (P < .05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (P > .05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (P < .05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3). MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

The comparison of visibility of the maxillary sinus septa between cone-beam computed tomography scans and panoramic radiograph images as dependent on the cortical bone thickness: a retrospective comparative study.

To analyze the visibility of the maxillary sinus septa (MSS) in panoramic radiography (PR) versus cone beam computed tomography (CBCT) and to investigate whether the buccal cortical bone thickness (BT) or the septa dimensions influence their visibility. Corresponding PR and CBCT images of 355 patients were selected and examined for MSS visibility. The septa dimensions (width, height, depth) and the BT were measured. Results were analysed statistically. Comparing the corresponding regions on CBCT and PR, 170 MSS were identified; however, only 106 of these were also visible using PR. The MSS visibility was significantly higher on CBCT versus PR images (P1: p = 0.039, P2: p = 0.015, M1: p = 0.041, M2: p = 0.017, M3: p = 0.000), except region C (p = 0.625). Regarding the measurements of MSS dimensions, only the height in region M1 (p = 0.013) and the width in region P2 (p = 0.034) were significantly more visible on CBCT. The BT in the area of the MSS was found to have a marginal influence on its visibility on the PR images only in regions M3 and M1 (M3: p = 0.043, M1: p = 0.047). In terms of MSS visibility based on the dimensions, significance was found for all three influencing variables only in region P2 (width; p = 0.041, height; p = 0.001, depth; p = 0.007). There were only isolated cases of further significance: M3 for width (p = 0.043), M2 for height (p = 0.024), and P1 for depth (p = 0.034), no further significance was noted. MSS visibility appears significantly higher on CBCT versus PR images. It is concluded that the septa dimensions and BT can influence MSS visibility on PR images just in certain regions.

A Random Forest-based Automatic Classification of Dental Types and Pathologies using Panoramic Radiography Images

A Random Forest-based Automatic Classification of Dental Types and Pathologies using Panoramic Radiography Images

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble

BackgroundDental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. MethodThe first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. ResultsThe experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. ConclusionsAI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.

Case report of fractured artifact in the mandibular region: A radiologist's challenge to diagnosis

An object or form that is considered an artifact was created artificially and is not often visible on the radiograph. Inadequate handling of the film, chemical contamination, and other factors are among the causes of flaws and artifacts, according to the literature. Because they can resemble diseases or can cover up the region of interest, it is crucial to correctly identify these. This article focuses on a radiolucent object seen on a routine dental radiograph that looks similar to a fracture line on the mandibular right side. We also cover the different sources of artifacts and place emphasis on correctly recognizing them so they aren't mistaken for pathology. This article examines the situation of a woman who appeared to be 28 years old, a radiolucent image on the patient's right side, extending from the coronoid process to the mandibular ramus. The radiolucent artifacts seen in the cephalometric and panoramic radiographic images, in this case, showed a discrepancy between the results on the right mandibular actual condition.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images. This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets. The YOLOv5 deep learning model was trained to detect six fracture types in the mandible based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and .812, respectively). The trained algorithm achieved promising performance metrics for the automated detection of most fracture types, with the highest performance observed in detecting body and symphysis fractures. Machine learning can provide a potential tool for assisting clinicians in mandibular fracture diagnosis.

Carotid Artery Calcification Detected on Panoramic Radiography Is Significantly Related to Cerebrovascular Accident, Coronary Artery Disease, and Poor Oral Health: A Retrospective Cross-Sectional Study.

Panoramic radiography imaging modality is widely used by dentists for diagnosing dental and jaw conditions. It can also detect carotid artery calcification (CAC), indicative of calcified atherosclerotic plaques in the carotid arteries. This cross-sectional retrospective study at the University of Tennessee Health Science Center investigated the link between CAC identified on panoramic radiograph (PR) and cerebrovascular accident (CVA), coronary artery disease (CAD), and poor oral health. Data from 314 CAC patients collected from 2014 to 2023 included age at diagnosis, gender, and clinical histories of hypertension, hyperlipidemia, diabetes mellitus, CVA, CAD, and the decay, missing, and filled permanent teeth (DMFT) index. These patients were age- and gender-matched with non-CAC patients for analysis. The findings revealed high prevalences of hypertension (86.2%), hyperlipidemia (57.6%), diabetes mellitus (30.7%), CVA (15.5%), and CAD (28.7%) amongst CAC patients and the average DMFT index was 26.6. A comparative analysis of 276 matched controls demonstrated significant differences in hypertension (85.9% vs. 57.6%), hyperlipidemia (58.3% vs. 33.7%), diabetes (32.6% vs. 22.1%), CVA history (14.9% vs. 5.1%), CAD (26.1% vs. 9.8%), and DMFT scores (26.3 vs. 23.7), all indicating strong associations between CAC and these health conditions. The adjusted analysis showed that hypertension (aOR: 3.20 [95% CI: 2.06-5.07]), hyperlipidemia (aOR: 1.70 [95% CI: 1.14-2.50]), CVA (aOR: 2.20 [95% CI: 1.13-4.30]), and CAD (aOR: 2.10 [95% CI: 1.28-3.60]) were significantly associated with CAC. Notably, only 41.7% of the patients received a medical consultation after CAC detection on PR. It is crucial for dentists to refer patients for further evaluation.

The Relationship Between Hand Preference and Mandibular Asymmetry: A Preliminary Study

Objective: The objective of this study was to evaluate the relationship between functional laterality (hand preference) and mandibular asymmetry in skeletal Class I, normodivergent patients.&#x0D; Methods: 21 left-handed and 40 right-handed Class I normodivergent patients were included in the study. The hand preferences of the participants were determined by using the Oldfied hand preference questionnaire. Mandibular condylar height (CH), ramal height (RH), and total height (CH+RH) were measured on pretreatment panoramic radiographic images of the patients, and asymmetry values were obtained according to the formula of asymmetry indices. Data were analyzed with the Student’s t-test and Pearson chi-square.&#x0D; Results: Right and left CH values were greater in left-handed patients than in right-handed patients. Right and left RH and CH+RH values were greater in right-handed patients than in left-handed. However, there was no difference between the measurements (p &gt; .05). There was no significant relationship between the functional values and hand-use preference (p &gt; .05).&#x0D; Conclusions: Hand use preference was not associated with condylar, ramal, and total mandibular asymmetry.