Panoramic Radiographic Images Research Articles

Association of mandibular canine impaction and anatomical factors: A multicentre retrospective panoramic study in Latin America

Mandibular canine impaction is infrequent in dental eruption anomalies and treatment is very challenging. The aim of this multicenter retrospective panoramic study in Latin America was to evaluate panoramic radiographic imaging characteristics of mandibular canine impaction (impaction area, mandibular base contact, transmigration, impaction height and sex) and their associations. This cross-sectional study evaluated 212 digital panoramic radiographs from three radiological centres in Tingo Maria (Peru), Bogota and Tunja (Colombia). The study included children of both sexes with impacted mandibular canines. Mandibular alpha angle, contact with mandibular basal bone (MBB), impacted sector according to 10 sectors with an adaptation of the Ericson and Kurol method, presence of transmigration and the impacted height were measured and the relationship among these measures was analyzed. Fisher's exact test, Chi-square and binary logistic regression were used. (P<0.05). The mandibular canine impaction showed contact with the MBB (32.08%), dental transmigration (36.79%), mainly located at an apical (40.09%) and sub-apical (36.79%) level. Transmigration mainly occurred in sectors 6 (33.30%) and 10 (25.60%) (P<0.001). It was found that for each year of increase in age, the possibility of contact with the MBB decreased (β=0.89, P=0.010), and as the alpha angle increased by one degree the probability of contact with the MBB decreased (β=0.97, P=0.001) and the probability of transmigration increased (β=1.05, P<0.001). One third of the impacted canines were in contact with the MBB, while another third presented dental transmigration and were mainly located apically and subapically of the incisor roots. These imaging features should be taken into account when planning orthodontic treatment.

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.

Changes in Dickkopf-1, but Not Sclerostin, in Gingival Crevicular Fluid Are Associated with Peroral Statin Treatment in Patients with Periodontitis.

Background and Objectives: Periodontitis is marked by the destruction of alveolar bone. Sclerostin (SOST) and dickkopf-1 (DKK-1) act as inhibitors of the Wingless-type (Wnt) signaling pathway, a key regulator of bone metabolism. Recent studies have suggested that statins play a role in bone resorption and formation by influencing Wnt signaling. The aim of this study was to determine the levels of SOST and DKK-1 in periodontal patients with and without peroral statins treatment in their therapy. Materials and Methods: A total of 79 patients with diagnosed periodontitis were divided into two groups: 39 patients on statin therapy (SP group) and 40 patients without statin therapy as a control group (P group). The periodontal clinical examination probing (pocket) depth (PD) and gingival recession (GR) were measured, and approximal plaque was detected, while vertical and horizontal bone resorption was measured using a panoramic radiograph image. Clinical attachment loss (CAL) values were calculated using PD and GR values. Gingival crevicular fluid (GCF) was collected and used for measuring SOST and DKK-1 levels. A questionnaire was used to assess lifestyle habits and statin intake. Patients' medical records were used to obtain biochemical parameters. Results: There was no significant difference in sclerostin concentration between the SP and P group. DKK-1 values were significantly higher in the SP group compared to the control group (p = 0.04). Also, PD (p = 0.001) and GR (p = 0.03) were significantly higher in the SP group. The level of DKK-1 had a positive relationship with the PD, the greater the PD, the higher the level of DKK-1 (Rho = 0.350), while there was no significant association with other parameters. Conclusions: Peroral statins in periodontal patients are associated with GCF levels of DKK-1 but not with sclerostin levels.

Automatic for Generating Landmark Mandibular Panoramic Radiography Image

Automatic for Generating Landmark Mandibular Panoramic Radiography Image

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 important, which can be achieved by an accurate interocclusal record, which can we 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 fairly consistent bony landmark and can be used for correlating with the 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 oethopantomogram (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 inturn helps the patients satisfaction as it preserves the intraoral components and health of TMJ.

A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks.

Panoramic radiography imaging plays a crucial role in the diagnostic process of dental diseases. However, current artificial intelligence research datasets for panoramic radiography dental image processing are often limited to single-center and single-task scenarios, making it difficult to generalize their results. To address this, we present a multi-center, multi-task labeled dataset. In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People's Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specifically, it comprises 2555 images related to impacted teeth, 2735 images related to periodontitis, and 1246 images related to dental caries. In order to evaluate the performance of the dataset, we conducted benchmark tests for segmentation and classification tasks on our dataset. The results show that the presented dataset could be effectively used for benchmarking segmentation and classification tasks critical to the diagnosis of dental diseases. To request our multi-center dataset, please visit the address: https://github.com/qinxin99/qinxini .

Accuracy of CBCT in Preoperative Assessment of Impacted Canines vs. Panoramic Radiography

ABSTRACT Background: Accurate assessment of impacted canines is crucial for successful treatment planning. Radiographic techniques like Cone Beam Computed Tomography (CBCT) and Panoramic Radiography are commonly used, but their comparative accuracy remains under scrutiny. Materials and Methods: In this study, 50 patients diagnosed with impacted canines were enrolled. Both CBCT and Panoramic Radiography images were obtained for each patient using standard protocols. Two experienced dentists independently analyzed the images to determine the position of impacted canines and their relationship with neighboring structures. Results: The findings of this study revealed that CBCT provided superior accuracy in assessing the position of impacted canines compared to Panoramic Radiography. Specifically, CBCT demonstrated a mean accuracy rate of 89.5%, while Panoramic Radiography showed a mean accuracy rate of 72.3%. Moreover, CBCT allowed for better visualization of impacted canine angulation, depth, and spatial orientation. Panoramic Radiography, on the other hand, displayed limitations in precisely identifying the impacted canine’s position. Conclusion: The study’s outcomes underscore the higher accuracy of CBCT over Panoramic Radiography in the preoperative assessment of impacted canines. CBCT’s detailed imaging provides valuable insights for treatment planning, potentially leading to improved surgical outcomes. Although CBCT entails greater radiation exposure and cost, its benefits in accurate diagnosis and treatment planning justify its use in cases of impacted canines.

Panoramic Radiograph Generation and Image Reconstruction from Latent Vectors Using a Generative Adversarial Network.

In this study, StyleGAN2 was trained with panoramic radiographs, and original images were projected into the latent space of StyleGAN2. The resulting latent vectors were input into StyleGAN2, and corresponding images were generated to reconstruct the original images. The original and reconstructed images were evaluated by pediatric dentists and found to be similar. Our results suggest that StyleGAN2 could be applied to the anonymization and data compression of medical images.

Does the type of impacted third molar induce the formation of caries on the distal surface of the second molar? A different outcome and interpretation

Introduction: Investigators have different ideas about whether or not the third molars should be extracted as a preventive measure. The aim of this study is to evaluate the relationship of impacted third molar with the frequency of caries on the distal surface of the second molar.&#x0D; Material and Methods: In this prospectively designed study, oral examination and panoramic radiographic images of 438 patients were performed. All of the second and third molars on the mandible are classificated by the eruption levels and angulation degrees. The presence of caries on the distal surface of the second molars was determined with the help of ICDAS-II criteria. Pearson Chi-Square and McNemar test was used to compare categorical variables and both segments.&#x0D; Results: The participants in the study ranged in age from 18 to 62, with a mean age of 28.33± 9.2 years. While a total of 1752 teeth were examined and both segments were evaluated for angulation type and level of eruption. The most frequent angulation type was vertical, and the most frequent eruption level was A. Mesioangulation was the most common type of angulation associated with caries in adjacent teeth.There was a statistically significant variation in the prevalence of caries between the two segments (p

Reduction of cervical vertebra ghost images in panoramic radiography using vertical dual exposure.

To evaluate the image quality of vertical dual-exposure panoramic radiography (PR), which merges two PR images taken at different focus heights to reduce ghost images of cervical vertebrae (CV) and intervertebral spaces (IVS) in the incisor region. PR images of an aluminum block, a CV phantom and a human head phantom were taken at 0 mm and merged with and subtracted from PR images taken at other heights (0, 5, 10, 15, and 20 mm) to create new images, e.g., Merg0 + 15 mm and Sub0 - 10 mm. The subtracted images were analyzed subjectively according to the uniformity on the line profile. Merged images were evaluated subjectively by six raters to determine the influence of the ghost images. Objective evaluation revealed a positional shift in the ghost images according to the height of the focus for both phantoms. In the subjective evaluation, the normal PR (Merg0 + 0 mm) showed the worst score, indicating strong influence of CV and IVS ghost images. The vertical dual-exposure PR method, which merges PR images taken at the normal position and a higher X-ray focus, can reduce CV and IVS ghost images in the incisor region.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis.

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

The cumulative survival rate of sandblasted, large-grit, acid-etched dental implants: a retrospective analysis.

This retrospective study aimed to assess the long-term cumulative survival rate of titanium, sandblasted, large-grit, acid-etched implants over a 10-year follow-up period and investigate the factors affecting the survival rate and change in marginal bone loss (MBL). The study included 400 patients who underwent dental implant placement at the Department of Periodontology of Seoul National University Dental Hospital (SNUDH) between 2005 and 2015. Panoramic radiographic images and dental records of patients were collected and examined using Kaplan-Meier analysis, Cox proportional hazards regression analysis, and multiple regression analysis to determine the survival rates and identify any factors related to implant failure and MBL. A total of 782 implants were placed with a follow-up period ranging from 0 to 16 years (mean: 8.21±3.75 years). Overall, 25 implants were lost, resulting in a cumulative survival rate of 96.8%. Comparisons of the research variables regarding cumulative survival rate mostly yielded insignificant results. The mean mesial and distal MBLs were 1.85±2.31 mm and 1.59±2.03 mm, respectively. Factors influencing these values included age, diabetes mellitus (DM), jaw location, implant diameter, bone augmentation surgery, and prosthetic unit. This study found that the implant survival rates at SNUDH fell within the acceptable published criteria. The patients' sex, age, DM status, implant location, implant design, implant size, surgical type, bone augmentation, and prosthetic unit had no discernible influence on long-term implant survival. Sandblasted, large-grit, acid-etched implants might offer advantages in terms of implant longevity and consistent clinical outcomes.

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.