Dental Radiographs Research Articles

Occurrence of radiopaque and mixed lesions at periapical region in patients with rheumatoid arthritis and ankylosing spondylitis: a retrospective study

BackgroundRheumatoid arthritis (RA) and ankylosing spondylitis (AS) have different effects on bones, cartilage and joints, sometimes destroying the spine and joints, and other times causing new bone formation. This study aimed to evaluate the effects of RA and AS on the types (radiolucent, radiopaque and mixed) of periapical lesions in jaw bones.MethodsThis study included 708 individuals (97 with AS, 327 with RA and 284 healthy controls (C)) and a total of 17,118 teeth (AS: 2,442; RA: 7,638; C: 7,038). The number of teeth, extracted teeth and teeth with root canal treatment and the presence of radiopaque, radiolucent and mixed periapical lesions were recorded from dental panoramic radiographs. Kruskal–Wallis and chi-square tests were used for statistical analysis.ResultsThe frequency of radiopaque lesions in the AS and RA groups was similar (p > 0.05) and significantly higher than in the C group (p < 0.05) (AS: 13.4%; RA: 6.1%; C: 2%). Mixed lesions (AS: 3.1%; RA: 4.0%; C: 0.4%) were statistically significantly higher for the RA group compared to the C group (p < 0.05), while the AS–C and AS–RA groups were similar (p > 0.05). There was no significant difference in terms of radiolucent lesions among groups (p > 0.05).ConclusionRadiopaque apical lesions were frequent in RA and AS patients, while mixed lesions were significantly higher in RA patients.

Panoramic dental X-ray exposure leads to oxidative stress, inflammation and apoptosis-mediated developmental defects in zebrafish embryos

Panoramic x-ray units are widely used in dental radiodiagnostics. Patients are exposed to relatively low radiation doses with panoramic imaging, but considering lifetime frequency of exposure, even a small risk can have serious health consequences. Our aim was to assess the effects of panoramic x-rays at two different exposure times on developing zebrafish embryos, focusing on oxidative stress, inflammation, apoptotic pathways, and development. Zebrafish embryos were divided into three groups: control, standard panoramic (SPE, 5.5 s exposure time) and pedodontic panoramic x-ray group (PPE, 4.8 s exposure time). Optically stimulated luminescence dosimeters were used to measure absorbed doses. Mean radiation doses for SPE and PPE were 7.83 mSv and 5.83 mSv respectively. At the end of 96 h post-fertilization, lipid peroxidation (LPO), nitric oxide (NO), reduced glutathione (GSH), glutathione S-transferase and superoxide dismutase were measured in the embryos. Expressions of genes related with inflammation (tnfα, il6, ill15, il21), immunoregulation (ifng) and apoptosis (p53, bax, casp2, casp3, casp8) were determined by RT-PCR. Even at reduced doses at high-speed mode, developmental toxicity was observed in both groups as evidenced by decreased pigmentation, yolk sac oedema, and spinal curvature. While deterioration of oxidant-antioxidant balance, suppression of immune response, induction of inflammation and apoptosis were observed through increased LPO, NO, decreased GSH, ifng, and increased expressions of genes related with inflammation and apoptosis, these effects were more pronounced in the SPE group. These results demonstrate the influence of exposure time and indicate the need for further consideration of optimal panoramic modes from a radiation-induced damage perspective.

Single Crown Restorations Supported by One-Piece Zirconia Dental Implants: Case Series with a Mean Follow-Up of 58 Months

The main aim of this case series was to report the clinical and radiographic outcomes of 22 one-piece zirconia dental implants positioned in 19 patients to restore single edentulisms and followed up for at least 2 years. The mean observation period was 58.18 months. At the last follow-up visit, no issues, such as foreign body sensation, discomfort or pain, were reported by the patients. No implant showed signs of infection with suppuration or implant mobility. Marginal bone levels (MBL) were assessed through standardized dental radiographs and a dedicated software. The mean distance between the implant head and the first detectable bone to implant contact was calculated at the mesial and distal aspect of each implant. The mean MBL at baseline was 1.82 ± 0.63 mm while the mean MBL at the last follow-up visit was 2.57 ± 0.72 mm. The results obtained in the present case series over a mean follow-up period of 58.18 months (range 27–96) showed that one-piece zirconia dental implants could be an alternative option to support single crowns in patients requiring metal-free restorations. Nevertheless, further better-controlled research featuring an adequate study design and longer follow-up is needed in order to clarify advantages and limitations which are related to this treatment modality.

Assessment of Panoramic Radiograph Errors: An Evaluation of Patient Preparation and Positioning Quality at Soelastri Dental and Oral Hospital

Background: Dental radiography is an important component of comprehensive patient care. One of the most frequently used extraoral radiographic techniques is panoramic radiography. Panoramic radiographs lose value when the quality of the diagnostic radiographs is poor. In their application, there are still many errors that occur in panoramic radiographs. Objective: This study aimed to determine the quality and frequency of panoramic radiograph errors due to patient preparation and positioning errors at the Soelastri Dental and Oral Hospital, Universitas Muhammadiyah Surakarta (RSGM Soelastri UMS), Indonesia. Methods: This type of research is descriptive, quantitative, observational, and takes a retrospective approach. A total of 312 panoramic radiographs of patients at RSGM Soelastri UMS from January to December 2021 who met the inclusion and exclusion criteria were taken at random stages and evaluated. Radiographs were compared with ideal quality, given a National Radiological Protection Board (NRPB) rating, and the frequency of errors that occurred was evaluated. Results: The most common rating obtained on panoramic radiographs in this study was 2 of 173 (55.45%). The frequency of preparation errors was 1.22%, while the frequency of patient position errors was 98.78%. The highest patient position error was not placing the tongue on the palate, which was 49.68%. Conclusion: The quality of panoramic radiographs at RSGM Soelastri UMS in the period from January–December 2021 generally has a rating of 2, according to the NRPB, and there are still errors in patient preparation and positioning.

Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph

Background/PurposeThe preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs). Materials and methods336 periapical radiographic images (teeth: 390) between January 2017 and December 2019 were collected and de-identified. All periapical radiographic image datasets were divided into training dataset (n = 82, teeth: 123) and test dataset (n = 336, teeth: 390). For creating an optimal deep CNN algorithm model, the training datasets were directly used for the segmentation and individual tooth detection. To evaluate the diagnostic power, we calculated the degree of alveolar bone loss deviation between our proposed method and ground truth, the Pearson correlation coefficients (PCC), and the diagnostic accuracy of the proposed method in the test datasets. ResultsThe periodontal bone loss degree deviation between our proposed method and the ground truth drawn by the three periodontists was 6.5 %. In addition, the overall PCC value of our proposed system and the periodontists’ diagnoses was 0.828 (P < 0.01). The total diagnostic accuracy of our proposed method was 72.8 %. The diagnostic accuracy was highest for stage III (97.0 %). ConclusionThis tool helps with diagnosis and prevents omission, and this may be especially helpful for inexperienced younger doctors and doctors in underdeveloped countries. It could also dramatically reduce the workload of clinicians and timely access to periodontist care for people requiring advanced periodontal treatment.

Insights on the digitalisation of dental practices: A cross-sectional pilot study in Hesse.

Even though today, many fields in dentistry allow digital processes, analogue procedures are still widely used. This cross-sectional pilot study aimed to survey insights on the digitalisation of dental practices using the example of Hesse. Between April and June 2022, 4840 active practicing dentists registered by the State Dental Association of Hesse were invited via e-mail to fill out an online questionnaire regarding their technical requirements in dental practice, dental treatment procedures, and attitude towards digitalisation in dentistry. Demographic questions were asked. Besides descriptive statistics, correlations were analyzed (P < 0.05). Questionnaires of 937 dentists (279 female, 410 male, four inter/divers, 244 no answer; mean age of 51.4 ± 10.4 years) were examined representing a respond rate of 19.36%. In the area of practice administration and dental radiography, the majority of the dentists surveyed is already working digitally, which is predominantly assessed as a positive development. Already one third of the respondents state that they use an intraoral scanner for dental treatments, but the indication is mainly limited to smaller restorations. However, many dentists rate the use of social media accounts and telemedicine rather negative. Within the limitation of this pilot study, many processes especially in dental treatments are still analogue. However, 60% of the participants plan digitalisation of their dental practices within the next five years, which indicates a clear shift from analogue to digital dentistry.

Diagnostic Study of Mandibular Cortical Index Classification Using Dental Cone-Beam Computed Tomography Findings: A Preliminary Cross-Sectional Study

The prevalence of osteoporosis is high, reportedly affecting 200 million people worldwide. A major problem associated with osteoporosis is that femoral fractures cause a decline in general function and loss of independence, greatly reducing patients’ quality of life. Notably, osteoporosis is an asymptomatic chronic metabolic disease, and its detection is thus often delayed. Interestingly, mandibular cortical index (MCI) classification using dental panoramic radiography is reportedly useful for early detection of osteoporosis. However, this visual classification method can lead to differences in diagnoses among surgeons. The aim of this preliminary study was to analyze cone-beam computed tomography (CBCT) data and examine an objective MCI classification using the findings obtained. MCI classification (classified as C1, C2, or C3) was performed by three examiners on 70 women (91 sites) aged ≥20 years. The mandibular cortical width of all sites was measured using CBCT images. The results showed that the mandibular cortical width was not particularly correlated with age or number of present teeth, and no significant quantitative differences were found between C1 and C2. However, coronal CBCT images of C2 revealed multiple characteristic trabecular bone structures. These structures may be an important finding affecting the classification of two-dimensional dental panoramic radiography images.

Pseudo labelling workflow, margin losses, hard triplet mining, and PENViT backbone for explainable age and biological gender estimation using dental panoramic radiographs

This study aimed to estimate human age and gender from panoramic radiographs using various deep learning techniques while using explainability to have a novel hybrid unsupervised model explain the decision-making process. The classification task involved training neural networks and vision transformers on 706 panoramic radiographs using different loss functions and backbone architectures namely ArcFace, a triplet network named TriplePENViT, and the subsequently developed model called PENViT. Pseudo labeling techniques were applied to train the models using unlabeled data. FullGrad Explainable AI was used to gain insights into the decision-making process of the developed PENViT model. The ViT Large 32 model achieved a validation accuracy of 68.21% without ArcFace, demonstrating its effectiveness in the classification task. The PENViT model outperformed other backbones, achieving the same validation accuracy without ArcFace and an improved accuracy of 70.54% with ArcFace. The TriplePENViT model achieved a validation accuracy of 67.44% using hard triplet mining techniques. Pseudo labeling techniques yielded poor performance, with a validation accuracy of 64.34%. Validation accuracy without ArcFace was established at 67.44% for Age and 84.49% for gender. The unsupervised model considered developing tooth buds, tooth proximity and mandibular shape for estimating age within deciduous and mixed dentitions. For ages 20–29, it factored permanent dentition, alveolar bone density, root apices, and third molars. Above 30, it notes occlusal deformity resulting from missing dentition and the temporomandibular joint complex as predictors for age estimation from panoramic radiographs.Graphical abstract

Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm

Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method’s feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes.

Novel reconstruction method of angle-limited backprojection (ALBP) for low-dose dental panoramic X-ray imaging

Panoramic radiography is a popular two-dimensional X-ray imaging modality in dentistry. It produces a single image of the overall facial view, including the maxillary and mandibular arches and supporting structures. Panoramic images are typically reconstructed using the shift-and-add (SAA) algorithm, in which only structures within a certain image layer are in focus, and others are out of focus on the panoramic image. As the clinical use of panoramic radiography has grown rapidly, radiologists are continuously seeking ways to reduce the radiation dose to patients. This study proposes a novel panoramic reconstruction method called the angle-limited backprojection (ALBP) algorithm for low-dose panoramic X-ray imaging. In ALBP, X-rays that meet the angular criterion established in this study are selected from the measured panoramic projection data and then backprojected onto the spherical voxels placed along the dental arch, as in a typical computed tomography reconstruction. The angular criterion selects X-rays that pass through a given spherical voxel and simultaneously have directional vectors within a prespecified angular range concerning the directional vector at the corresponding source position. To validate the efficacy of the proposed reconstruction method, we conducted a numerical simulation and experiment and investigated the image characteristics. Panoramic images were reconstructed using the SAA and ALBP algorithms with full and half (i.e., half radiation dose) panoramic projections, and their image quality was quantitatively evaluated using the intensity profile and universal quality index (UQI). The image quality of the ALBP with half projections was similar to that of the SAA with full projections, indicating the efficacy of the proposed method. Accordingly, our results demonstrated the potential for radiation dose reduction in panoramic X-ray imaging.

System of gender identification and age estimation from radiography: a review

&lt;span lang="EN-US"&gt;Under extreme conditions postmortem, dental radiography examinations can play an essential role in individual identification. In forensic odontology, individual identification traditionally compares antemortem dental records radiographs with those obtained on postmortem examination. As such, these traditional methods are vulnerable to oversights or mistakes in the individual identification of unidentified &lt;span&gt;bodies. Digital technology can develop forensic odontology well. An automatic individual identification system is needed to support the forensic odontology process more easily and quickly because there are still opportunities to be created. We aimed to review the complete range of recent developments in identifying individuals from panoramic radiographs. We study methods in gender identification, age estimation, radiographic segmentation, performance analysis, and promising future directions&lt;/span&gt;.&lt;/span&gt;

Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs

ObjectivesThe objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. Methods292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): GX-ray, GX-ray+clinical data, GX-ray+AI, GX-ray+clinical data+AI. ResultsThe results showed that AI surpassed the performance of students in all groups with an F1-score of 0.71 (P < 0.001). The students’ F1-score in GX-ray+AI and GX-ray+clinical data+AI with model prediction (0.61 and 0.61 respectively) was slightly higher than the F1-score in GX-ray and GX-ray+clinical data (0.58 and 0.59 respectively) with a borderline statistical significance of P = 0.054. ConclusionsAlthough the AI model had much better performance than all groups, the participants when given AI prediction, benefited only ‘slightly’. AI technology seems promising, but more explainable AI predictions along with a 'learning curve' are warranted.

Dental management of a 3-year-old uncooperative child under general anaesthesia: a case report

Dental care is medically necessary for the purpose of eliminating and preventing orofacial disease, infection and pain, restoring the form and function of the dentition, and correcting facial disfiguration or dysfunction – American Academy of Pediatric Dentistry (AAPD). Lack of cooperation in young paediatric patients can be a confounding factor for dental rehabilitation procedures and even examination or radiographs. To complete necessary dental radiographs and/or treatment in young, uncooperative children general anaesthesia or deep sedation is often required. This article presents the case of a 3-year-old girl child with a complaint of sharp shooting pain in upper and lower left back tooth region and was extremely uncooperative. She was diagnosed with early childhood caries; full mouth rehabilitation was done under general anaesthesia. Clinical examination, classification and treatment plan are discussed based on the review of current literature available.

Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews.

This overview of systematic reviews aimed to establish the current state of knowledge on the suitability of artificial intelligence (AI) in dental panoramic radiograph analysis and illustrate its changes over time. Medical databases covered by the Association for Computing Machinery, Bielefeld Academic Search Engine, Google Scholar, and PubMed engines were searched. The risk of bias was assessed using ROBIS tool. Ultimately, 12 articles were qualified for the qualitative synthesis. The results were visualized with timelines, tables, and charts. In the years 1988-2023, a significant development of information technologies for the analysis of DPRs was observed. The latest analyzed AI models achieve high accuracy in detecting caries (91.5%), osteoporosis (89.29%), maxillary sinusitis (87.5%), periodontal bone loss (93.09%), and teeth identification and numbering (93.67%). The detection of periapical lesions is also characterized by high sensitivity (99.95%) and specificity (92%). However, due to the small number of heterogeneous source studies synthesized in systematic reviews, the results of this overview should be interpreted with caution. Currently, AI applications can significantly support dentists in dental panoramic radiograph analysis. As systematic reviews on AI become outdated quickly, their regular updating is recommended. PROSPERO registration number: CRD42023416048.

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs.

The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female. Data preprocessing included anonymizing the exams, extracting pertinent information from the reports, such as sex, age, type of dentition, and number of missing teeth, and organizing the data into a PostgreSQL database. Two neural network architectures, a standard CNN and a ResNet, were utilized for sex classification, with both undergoing hyperparameter tuning and cross-validation to ensure optimal performance. The CNN model achieved 95.02% accuracy in sex estimation, with image resolution being a significant influencing factor. The ResNet model attained over 86% accuracy in subjects older than 6 years and over 96% in those over 16 years. The algorithm performed better on female images, and the area under the curve (AUC) exceeded 96% for most age groups, except the youngest. Accuracy values were also assessed for different dentition types (deciduous, mixed, and permanent) and missing teeth. This study demonstrates the effectiveness of an AI-driven tool for sex determination using PR and emphasizes the role of image resolution, age, and sex in determining the algorithm's performance.

Fundamental research for dose control during supine dental panoramic radiography

ObjectiveThis study aimed to control radiation doses when using a portable supine dental panoramic radiography system by measured the scattered doses. MethodThe study used LPX7007 (Asahi Roentgen) for the panoramic radiography system. The subjects comprised a cylinder phantom (QualitA) and a RANDO Phantom (Alderson). The semiconductor dosimeter was an X2 survey sensor (RaySafe). The phantom was set at a height of 1 m from the floor, and the sensor was set at 1 m from the floor at the genital level and 1.5 m at the lens level. Measurements were taken at 30°intervals clockwise from 0°at distances of 0.5 m and 1 m in radius around the phantom. The occupational exposure range was defined as 0 ± 30° and the public exposure range was defined as the occupational exposure range and 30° to 150° and 210° to 330° as the public exposure range. ResultThe highest doses were observed in the 120° and 240° directions, and the lowest in 0° ± 30° range. The lowest limit number of images taken in the occupational exposure range was 130 images at a distance of 0.5 m, 452 images at 1 m at the lens level for the cylinder phantom, and 320 images at 0.5 m and 1098 images at 1 m for the RANDO Phantom. In the public exposure range at the genital level, there was one image at 0.5 m and six images at 1 m for the cylinder phantom, and two images at 0.5 m and eight images at 1 m for the RANDO Phantom. ConclusionWe found that radiation exposure can be reduced by keeping a distance from the subject, avoiding working at 120° and 240° and staying within 0° ± 30° behind the panoramic radiography system.

Caries detection and characterization in pediatric patients using iTero 5D near-infrared technology

Near-infrared imaging (NIRI) has been proposed as an alternative to radiographs and uses nonionizing radiation in the near-infrared spectrum to differentially scatter light off tooth surfaces and generate images allowing interproximal caries detection. The new iTero 5D Element Scanner (Align Technology, Santa Clara, Calif) has integrated NIRI capture and viewing technology but has not been specifically studied in a pediatric population. Therefore, this study aimed to assess clinicians' abilities to detect and characterize caries in pediatric patients using this instrument. Bitewing (BW) radiographs and an intraoral scan were captured on 17 pediatric patients (344 surfaces were analyzed). Data were randomized and graded by 5 calibrated clinicians individually with 2 different rounds of grading. The reliability of lesion characterization (ie, grade) among examiners was poor to fair in both systems, whereas the reliability of caries detection was moderate. Both systems had a high specificity and low sensitivity. The reliability of the characterization of the combined dataset was moderate to substantial, whereas, for detection, it was substantial. When using either BW or NIRI analysis, reliability is relatively poor, and clinicians are more likely to correctly identify a healthy tooth surface when compared with a carious surface. There is a small difference in error rate between BW and NIRI systems that is not likely to be clinically significant. When NIRI and BW data are combined, clinician agreement for both lesion characterization and detection increases significantly.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images.

Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

Factors Influencing Anxiety Levels During Dental Radiographic Examination Among Dental Students

Introduction: : Dental anxiety was found to be related to the radiographic procedure and can be influenced by sociodemographic characteristics of patients such as age, gender, level of education, and provoking factors such as past dental examination experience. Evaluating the anxiety regarding dental radiographic examination could be beneficial in ensuring the procedure’s continuance. This study aimed to measure the radiology knowledge among dental students and its association with anxiety due to dental radiographic examination. Methods: A cross-sectional study was conducted to collect the data using a questionnaire packet that included a knowledge and anxiety questionnaire, demographic data, and information about the experience of dental radiographic examination. A total sampling of 1st and 2nd year undergraduate and clerkship dental students at the Faculty of Dentistry, Universitas Gadjah Mada, Indonesia was selected based on inclusion and exclusion criteria. Anxiety-related factors were assessed using descriptive statistics and multiple linear regression conducted with STATA 15.1. Results: Results showed that knowledge and anxiety were negatively correlated but the correlation was not statistically significant. Gender, radiographic technique, and presence/absence of a companion during the examination were not correlated with anxiety. Meanwhile, the anxiety level was negatively and significantly correlated with age and examination frequency (p &lt; 0.05). Multiple linear regression results showed strong evidence that age and examination frequency were the main factors for predicting anxiety. Conclusion: Although age, radiographic examination frequency, and educational level are significant bivariate predictors of anxiety, subsequent regression analysis indicated that only age and radiographic examination frequency influence anxiety.

Treatment of a nasal angiofibroma in a cat using stereotactic body radiation therapy.

A 14-year-old cat presented with right-sided epistaxis, right facial swelling, hyporexia, and sneezing. A right nasal mass was diagnosed based on dental radiography and computed tomography (CT), and nasal angiofibroma was diagnosed based on histopathology. Treatment consisted of stereotactic body radiation therapy in three consecutive daily doses. Self-limiting grade 3 oral mucositis developed which resolved within 6 weeks. Recheck CT 169 days after treatment confirmed a partial response by RECIST(1) based on digital CT measurements . Disease progression was confirmed on CT 642 days after treatment, per RECIST criteria, with the longest tumor diameter measuring 3.4cm.