Maxillofacial Radiology Research Articles

Evaluation of Panoramic Radiography Diagnostic Accuracy in the Assessment of Interdental Alveolar Bone Loss Using CBCT.

Alveolar bone loss (ABL) and periodontal lesions are common diseases that have an undeniable effect on teeth maintenance and health. Current diagnostic methods include probing, intraoral radiography, and panoramic radiography; each has its limitations. In this study, we aimed to assess the diagnostic accuracy of panoramic radiography in the diagnosis of interdental ABL. In this cross-sectional study, panoramic and cone-beam computed tomography (CBCT) images from 80 patients were collected from the archives of an oral and maxillofacial radiology center. The amount of ABL was obtained by measuring the distance from the Cemento-Enamel Junction (CEJ) to the alveolar bone crest on both panoramic and CBCT images. Patients were divided into healthy (ABL ≤ 2 mm) and diseased (2 mm < ABL) groups in terms of periodontal disease. There was no statistically significant difference in the average ABL in the premolar, maxillary molar, and mandibular molar areas between the two techniques. However, in other areas, the ABL size was significantly lower in the panoramic view (p < 0.05). Also, the panoramic technique correctly recognized 89.1% of normal and 88.4% of ABL cases. The overall accuracy of panoramic radiography in the diagnosis of ABL was 85%, indicating the good accuracy of this technique. In maxilla, the highest diagnostic accuracy of the panoramic technique was in the molars, and the lowest was in the incisors. In the mandible, the highest and lowest diagnostic accuracy of the panoramic technique was related to molars and premolars, respectively. According to the kappa statistic, there was a significant good to very good agreement between the two types of techniques in all maxillary areas (p < 0.001). Panoramic radiography is accurate in showing ABL. Measuring ABL in the posterior mandibular areas in panoramic radiographs is quite reliable; however, in general, digital panoramic radiography shows less ABL than the actual amount.

A Comparison of Deep Learning vs. Dental Implantologists in Cone-Beam Computed Tomography-Based Bone Quality Classification.

Bone quality assessment is crucial for pre-surgical implant planning, influencing both implant design and drilling protocol selection. The Lekholm and Zarb (L&Z) classification, which categorizes bone quality into four types based on cortical bone width and trabecular bone density using cone-beam computed tomography (CBCT) data, lacks quantitative guidelines, leading to subjective interpretations. This study aimed to compare the performance of deep learning (DL)-based approaches against human examiners in assessing bone quality, according to the L&Z classification, using CBCT images. A dataset of 1100 CBCT cross-sectional slices was classified into four bone types by two oral and maxillofacial radiologists. Five pre-trained DL models were trained on 1000 images using MATLAB®, with 100 images reserved for testing. Inception-ResNet-v2 achieved the highest accuracy (86.00%) with a learning rate of 0.001. The performance of Inception-ResNet-v2 was then compared to that of 23 residency students and two experienced implantologists. The DL model outperformed human assessors across all parameters, demonstrating excellent precision and recall, with F1-scores exceeding 75%. Notably, residency students and one implantologist struggled to distinguish bone type 2, with low recall rates (48.15% and 40.74%, respectively). In conclusion, the Inception-ResNet-v2 DL model demonstrated superior performance compared to novice implantologists, suggesting its potential as an supplementary tool for cross-sectional bone quality assessment.

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application. 825 condyles of 490 CBCT images from 3 centers of Stomatology hospitalaffliated to Zhejiang University School of Medicinewere collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed. The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755mm (cortex), 0.826mm (marrow), and 0.760mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved. The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

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.

Impacted mandibular third molars: a comparison of orthopantomography and cone-beam computed tomography imaging in predicting surgical difficulty.

This study investigated the predictive value of orthopantomography (OPG) for the difficulty of extracting impacted mandibular third molars, in comparison with cone-beam computed tomography (CBCT). In this descriptive quantitative investigation, two oral and maxillofacial radiologists evaluated OPG and three-dimensional CBCT images according to the Pell-Gregory and Winter classifications. The results for the classification were compared using the chi-square test, and the prediction of difficulty was assessed using the Pederson scale, with a significance level of p< 0.05. The study included 30 patients (14 men and 16 women), providing a total of 53 samples of impacted mandibular third molars. Of these, 30 (56.6%) were from the right side and 23 (43.4%) from the left. There was a statistically significant difference between the OPG and CBCT images concerning their relation to the mandibular ramus (p< 0.05). However, evaluations based on occlusal lines and angulation showed no significant differences (p> 0.05). According to the Pederson scale, significant differences were observed between OPG and CBCT in predicting extraction difficulty (p< 0.05). CBCT offered a more accurate assessment of the surgical difficulty associated with mandibular third molars than OPG. OPG views frequently failed to adequately visualize the region of the mandibular ramus, influencing the perceived difficulty of mandibular third molar surgery. In certain cases, the use of CBCT imaging is crucial.

Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning

BackgroundMaxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.ObjectiveThe aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.MethodsData set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.ResultsRecall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.ConclusionThis study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.

Charting Ethical Terrain: The Functon of Artificial Intelligence in Oral and Maxillofacial Imaging

The integration of artificial intelligence (AI) into maxillofacial imaging represents a significant advancement in diagnostics and therapy. This review explores the ethical implications of AI in this specialized area, addressing concerns such as data privacy, informed consent, and algorithmic bias. It highlights the potential benefits of AI for patient outcomes and clinical efficiency while acknowledging risks associated with reliance on automated systems. The review aims to establish a framework for ethical guidelines to ensure that AI enhances patient care. AI's application in various industries has gained momentum, with dentistry, particularly oral and maxillofacial radiology, emerging as a promising field. Recent studies have focused on convolutional neural networks for tasks such as image classification, detection, segmentation, and refinement. These AI systems support radiographic diagnosis, image analysis, forensic dentistry, and image quality enhancement. However, optimal performance requires large, well-labeled datasets, necessitating significant input from oral and maxillofacial radiologists, which can be time-intensive. For AI to be effectively integrated into clinical practice, several challenges must be overcome, including the creation of comprehensive open datasets, understanding AI judgment criteria, and addressing DICOM hacking threats. By developing solutions alongside AI advancements, the technology can significantly evolve, potentially transforming automated diagnosis, treatment planning, and tool development. Oral and maxillofacial radiologists will play a crucial role in shaping AI applications in their field, leveraging their expertise in interpreting radiographic images.

Clinical use of cone-beam computed tomography in Western Norway: a referral-based retrospective study.

To elucidate cone-beam computed tomography (CBCT) referral profiles in Western Norway. In all, 3,031 referrals to oral- and maxillofacial radiologist were reviewed. Patient data were retrieved retrospectively from electronic charts. The patient's age, gender, and perceived clinical indication were noted, as well as relevant medical and dental history and whether the referring clinician was a general dentist or held a clinical dental specialty. A total of 2,680 referrals fulfilled the inclusion criteria (UiB n = 1,471, and TkVestland, n = 1,209). The female:male ratio was 1,427:1,253. Mean age was 33 years - 35 years for females compared to 31 years for males (p < 0.001). The most common clinical indications were related to impacted teeth (29%), endodontic issues (17%), cleft lip palate (12%), and resorptions (10%). Less common were bone lesions, implant planning, trauma to the teeth or jaws, atypical orofacial pain, and temporo-mandibular joint (TMJ). The patient age-profiles mirrored differences in indications within the cohort. Most referrals were from specialist dentists such as orthodontists, oral surgeons, and endodontists. Interestingly, 543/2,680 (20%) referrals were from general dentists. Conclusions: Specialist dentists such as orthodontists, oral surgeons, and endodontists refer most patients for clinical indications such as impacted teeth, endodontic issues, and resorptions.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Morphometric evaluation of middle ear anatomical structures using full volume (12 × 9) CBCT scans: A retrospective study in Navi Mumbai

Introduction: The tympanic cavity contains three tiny bones, the malleus, incus, and stapes, which have a fundamental role in the transmission of sound. Recent research emphasizes the use of CBCT for the anatomic study of the temporal bone. The information about middle ear anatomy on CBCT scans is meager; hence, this retrospective study was conducted to identify and determine the various morphometrical parameters of the malleus using CBCT which can be helpful during reconstructive procedures for the otologic surgeon. Materials and Methods: The retrospective study was performed on 200 subjects (101 M and 99 F) in the Department of Oral and Maxillofacial Radiology. CBCT images of 200 subjects were studied in all planes to identify ossicular chain and the malleus to investigate the morphometric parameters in Indian Subjects. Result: The average of morphometric parameters shows that the mean length of the malleus is 7.2 mm, the mean width of the head of the malleus is 3.02 mm, length of superior semicircular canal is 4.90 mm, and length of external auditory canal is 19.15 mm in Navi Mumbai population. Conclusion: The obtained morphometric data of the malleus through our study will help the prosthesis maker in the formation of various implants and grafts required to treat various ear diseases.

Level of implementation of digital transformation in dental radiology centers in Metropolitan Lima: A cross-sectional study.

Digital transformation (DT) involves introducing digital technologies into business models in all areas. This research aimed to evaluate the level of implementation of DT using the digital health indicator in private radiology centers in Lima, Perú. Forty-five randomly selected radiology centers included in a database of 50 registered centers were evaluated. The inclusion criteria were having a web domain and institutional email address. They were digitally surveyed using a digital survey (HIMSS DHI Rapid) measuring four dimensions: interoperability, governance and workforce, predictive analytics, and person-enabled health. These indicators allowed determination of the digital transformation level of a radiological company. The level of implementation was measured quantitatively on a scale from 0 to 400, and the Kruskal Wallis test (p>0.05) was used to compare DT according to the geographical location of the centers. The digital health indicator obtained was 60.24 ± 43.14 out of 400 achievable points. The dimensional analysis in terms of interoperability was 24 ± 18.09, followed by governance and workforce at 23.44 ± 18.58, person-enabled health at 18.73 ± 15.63, and finally, predictive analysis at 16.18 ± 13.51. No significant differences were found in health indicator dimensions according to the geographical location (p>0.05). DT in maxillofacial radiology centers in Lima is at an initial level. Radiology centers should take this situation into account to have relevant information for making diagnostic and treatment decisions and to provide better preventive health policies to benefit the population. Key words:Digital transformation, digital health indicator, dental radiology.

Assessment of Diagnosis of Apical Root Fractures During Tooth Extraction Using Different Radiographic Techniques: An Ex-vivo Study

Objective: This study aimed to examine the diagnostic ability of different imaging techniques for apical root fractures that occur during tooth extraction by specialist dentists in different branches. Methods: Dry human mandibles used for education at Faculty of Dentistry and teeth extracted for routine treatment were used. After the root lengths were measured using a periodontal probe, the samples were adjusted to different lengths. These specimens were placed on a dry human mandible, and images were obtained and recorded using a periapical device, panoramic device, and computed tomography. Radiographs and recordings were performed by an oral and maxillofacial radiologist. The evaluation process was performed by an oral and maxillofacial radiologist, periodontologist, and oral and maxillofacial surgeon. Results: The diagnosis of 1 mm root presence and absence on periapical radiographs showed significant agreement among all observers. In the presence of 2 mm and 3 mm roots, all observers stated that the roots were present. On the panoramic radiographs, moderate agreement was observed in teeth with a 1 mm root. However, poor agreement between observers was observed for teeth with 2 mm and 3 mm roots. Cone-beam computed tomography (CBCT) was effective for the diagnosis of all observers. Conclusion: Consistent with the literature, the present study showed a higher interobserver agreement in CBCT. However, considering the patient's anxiety during the procedure, the duration of local anesthesia, and the surgeon's fatigue, two-dimensional radiographs are generally preferred over CBCT, which has a longer image processing time. Diagnosis using periapical radiographs was more effective than that using panoramic radiographs.

Prevalence of Middle Mesial Canal in Mandibular Molar Teeth Using CBCT

Introduction: Identification and completely cleaning the extra root canals are crucial for the success of endodontic treatment. Prevalence of the middle mesial canal (MMC) in mandibular molars is still a topic of debate due to the complex and diverse anatomy and morphology of these teeth. This study aimed to investigate the prevalence of MMC in mandibular molars using CBCT radiography. Methods: In this descriptive cross-sectional study, 300 CBCT images obtained between 1995 and 2019 in a private oral and maxillofacial radiology center in Yazd, Iran, were evaluated. The first and second mandibular molars were examined for the presence of MMC based on the patients' age, gender, tooth location, and type. Data were analyzed using SPSS 17 and the Chi-square. Results: Out of 300 mandibular molars (122 first molars and 178 second molars), MMC was found in only 18 teeth (6%). The prevalence of MMC was significantly higher in the first molars (18 cases) than in the second molars (0 cases). Twelve MMCs were observed in females and 6 in males. 12 MMCs were located in the left side and the rest in the right side. The mean age of individuals with MMC was 37.33±15.27. There was no statistically significant association between the presence of MMC and age, gender, or tooth location (P &gt; 0.05), but the prevalence of MMC was higher in the first molars than in the second molars (P = 0.0001). Conclusion: The prevalence of MMC in mandibular first molars was low in the studied population and was not detected in mandibular second molars. Nevertheless, the possibility of MMC should be considered during the treatment of teeth.

Detection of the Fixed Prostheses on Panoramic Images: An Artificial Intelligence Based Study.

To investigate the effectiveness of using YOLO-v5x in detecting fixed prosthetic restoration in panoramic radiographs. Descriptive study. Place and Duration of the Study: Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkiye from November 2022 to April 2023. For the labelling of fixed prosthetic restorations, 8,000 panoramic radiographs were evaluated using the YOLO-v5x architecture. In creating the dataset for this study, fixed prosthetic restorations were categorised as dental implant, pontic, crown, and implant-supported crown on dental panoramic radiographs. The labelled images were then randomly split into three groups: 80% for training, 10% for validation, and 10% for testing. The labelled panoramic images constituted the model's training dataset, and leveraging the knowledge acquired during this learning stage, the model generated predictions in the testing phase. The majority of labelling data were dedicated to crown restorations. The precision and sensitivity values of YOLOv5x were 0.99 and 0.98 for crowns, 0.98 and 0.99 for implants, 0.99 and 0.99 for pontics, and 0.99 and 0.99 for implant-supported crowns, respectively. The results obtained in this study demonstrate a satisfactory success rate of YOLO-v5x in detecting dental prosthetic restorations. The high precision and sensitivity of the model indicate its strong potential to enhance clinical professional performance and contribute to the development of more efficient dental health services. Artificial intelligence, Dentistry, Dental prosthesis, Panoramic radiography.

Anatomical characteristics of mental foramen and canal: A cone-beam computed tomography analysis.

So far, different studies have endeavored to evaluate the position and dimensions of mental foramen and canal using cone-beam computed tomography (CBCT) images with various results. This study aimed to assess the anatomical variations of the mental foramen and canal utilizing CBCT images. In this retrospective observational study, we investigated CBCT scans of 355 patients (710 terminal branches of mental canal and foramen) who were referred to a private dental and maxillofacial radiology center in Babol, during 2020-2022. We recorded different anatomical variations of mental foramina and canals on left (n=355) and right (n=355) mandibles. Most of the mental canals had a distal opening (n=334, 47.0%). The distance between the mental foramen and the lower mandibular border was greater on left mandible (13.92±3.73 mm) than on the right mandible (12.25±3.94 mm) (p<0.001). On left mandible, the vertical diameter of the mental foramen, as well as the distance between the mental foramen and the lower mandibular border, were significantly greater in men than in women. On right mandible, the distance between the mental foramen and the upper alveolar crest edge, as well as the distance between the mental foramen and the lower mandibular border, were significantly greater in men versus women. Finally, on right mandible, the vertical diameter of the mental foramen was significantly greater in subjects aged >45 years compared with those aged ≤45 (p=0.024). There were notable variations in the morphological characteristics of the mental foramen and canal, which should be considered by clinicians. Key words:Mental foramen, mental canal, cone-beam computed tomography.

Do the number of zirconia implants and the thickness of CBCT image reconstruction affect the detection of peri-implant bone defect? A diagnostic accuracy exvivo study.

To evaluate the influence of multiplanar reconstruction thickness on the detection of peri-implant bone defects with a standalone zirconia implant and compare it to when another implant is in the vicinity using cone-beam computed tomography (CBCT). Five dry human mandibles were used to create twenty implant sites in the second premolar and first molar regions. The OP300 Maxio was used to acquire CBCT images (90 kVp, 6.3 mA, 5 × 5 cm FOV, and 0.125 mm3 voxel size) before and after creating 3 mm peri-implant bone defects in the buccal aspect of the premolar region. Half of the scans featured a single zirconia implant in the premolar region, while the others had two implants in the premolar and molar regions. Three reconstruction thicknesses (0.125 mm, 1 mm, and 2 mm) were considered for the multiplanar reconstruction analyses. Five oral and maxillofacial radiologists assessed the detection of peri-implant bone defects using a 5-point scale. Diagnostic parameters were calculated and compared using Two-way ANOVA (α = .05). The studied factors showed no significant influence on the diagnosis of peri-implant bone defects (p > .05). Diagnostic performance was notably higher with a single implant, especially with a 2-mm reconstruction thickness (AUC = 0.88, sensitivity = 0.68, specificity = 0.94). Although the differences were not statistically significant, the results were more modest when two implants were present (AUC = 0.80, sensitivity = 0.58, specificity = 0.82). The presence of an adjacent zirconia implant and variations in reconstruction thickness did not influence the detection of 3 mm buccal peri-implant bone defects on CBCT images.

Incidence of Carotid Atheroma in Patients Received Radiotherapy in Neck Field-A Case Series Study

Introduction: Atheroma is a condition that affects various blood vessels, primarily forming at junctions of moderate to large vessels, such as the carotid artery, where it is known as carotid atheroma (CA). Atheroma can lead to myocardial infarction or stroke. Previous studies have shown that head and neck radiotherapy can induce CA, even in young patients. Early diagnosis of atheroma in panoramic views can help prevent ischemic attacks in the brain and potentially save lives. Case Presentation: Thirty-six patients were referred to Semnan Dental School for dental screening by a hematologist-oncologist. An oral medicine specialist ordered panoramic radiographs for each patient, and radiopaque lesions were detected in the para-spinal region in four patients. These lesions were diagnosed as Carotid Atheroma by an Oral and Maxillofacial Radiologist, and this case series was reported. Conclusions: Radiotherapy is a component of treatment for head and neck malignancies and can cause atherosclerosis in carotid junctions. Dentists should monitor panoramic views of patients receiving radiotherapy for any signs of suspicious lesions. If carotid atheroma is detected, the patient should be referred to a cardiovascular specialist for further evaluation and treatment.

Prevalence of Zygomatic Air Cell defects in a sample of 6-18-year-old patients in Saudi Arabia and Yemen: a retrospective study

ObjectivesThis study aimed to comprehensively analyze the prevalence and influencing factors of Zygomatic Air Cell Defects (ZACD) among pediatric and adolescent populations in Saudi Arabia and Yemen.Materials and methodsA cross-sectional retrospective study was conducted, utilizing digital panoramic radiographs of dental outpatients aged 6 to 18 years. The data were collected from registered digital databases of dental clinics in Al-Qassim, Saudi Arabia, and an oral and maxillofacial radiology center in Sana’a, Yemen, covering the period from July 2018 to September 2022. The radiographs were evaluated based on inclusion and exclusion criteria, and ZACD was assessed using standardized descriptions. Statistical analysis, including logistic regression, was employed to examine the impact of the associated factors.ResultsThe study encompassed a total of 3,169 participants, revealing an overall ZACD prevalence of 28.5%. Age emerged as a significant predictor (p ≤ 0.0001), with the likelihood of ZACD increasing as the age of the individual advances. Gender and geographic region did not exhibit statistically significant differences in ZACD prevalence.ConclusionThis study provides crucial insights into the prevalence of Zygomatic Air Cell Defects among pediatric and adolescent populations in Saudi Arabia and Yemen. It underscores the prevalence of ZACD and the notable influence of age on its occurrence. Additionally, the research challenges prior notions of gender and regional variations in ZACD prevalence, emphasizing the complexity of the factors involved. Early detection is essential to avoid unwanted complications during any surgical intervention in this area.

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.