RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study.

Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.

Demirjian's and Cameriere's Methods for the Assessment of Dental Age Estimation in Children from a Southern Brazilian City.

The chronological age estimation of living individuals is a crucial part of forensic practice and clinical practice, such as in orthodontic treatment. It is well-known that methods for age estimation in living children should be tested on different populations. Ethnic affiliations in Brazil are divided into several major groups depending on the region, with the south of Brazil being known for its German immigration. (1) Background: This study aimed to evaluate the correlation between chronological age and dental age using Demirjian's method and Cameriere's method in a group of children from Joinville, South Brazil to investigate if both methods can be used to estimate dental age in this population. (2) Methods: The sample consisted of 229 panoramic radiographs (119 were males and were 110 females) from Brazilian children (ages ranging from 6 to 12 years). The chronological age at the time of the panoramic radiographic exam was calculated for each child. The dental age was estimated according to Demirjian's method and Cameriere's method. All continuous data were tested for normality by using the Shapiro-Wilk test. The Pearson correlation coefficient test was applied. An alpha of 5% (p < 0.05) was used for all analyses. (3) Results: The mean chronological age was 8.75 years. According to Demirjian's method, the mean dental age was 9.3 years, while according to Cameriere's method, the mean dental age was 8.66 years. A strong correlation between chronological age and dental age according to Demirjian (r = 0.776 and p < 0.0001) and Cameriere (r = 0.735 and p < 0.0001) was observed for both genders. (4) Conclusions: Both methods presented a good correlation with chronological age in the studied population and could be used to assess dental age in this population.

A Cone-Beam Computed Tomography-Based Evaluation of Root Dilaceration in Permanent Premolars: A Retrospective Study.

Statement of the Problem: As a developmental disorder characterized by an abnormal bend and angle in the longitudinal axis of the tooth root, dilaceration can cause complications in routine dental procedures such as endodontics, orthodontics, and surgical treatments. Purpose: The purpose of this study was to investigate the prevalence of dilaceration in maxillary and mandibular premolar teeth in a population of Shiraz city based on cone-beam computed tomography (CBCT). Materials and Method: This is a retrospective cross-sectional study on 927 premolar teeth and 132 CBCT radiographs of patients obtained from four private radiology clinics in Shiraz (Iran). In this study, the presence, location, direction, and severity of dilaceration in premolar roots as well as its relationship with gender were investigated. Chi-square and Fisher tests were used to analyze the data. Results: The results showed that 17% of the studied 927 teeth had dilaceration. The prevalence of dilaceration was significantly higher in women than in men (20.3% vs. 13.6%, p= 0.005). The dilaceration rates were significantly higher in the mandibular first and second premolar teeth (31.6% and 26%, p= 0.002) than in the other teeth. In addition, the highest prevalence was in the distal direction with mild severity in the apical third of the root (p< 0.001). Conclusion: According to the results of this study, the prevalence of dilaceration was relatively high in mandibular premolar teeth especially in women.

Mandibular bone mass density in a medieval population and its relationship with stable isotopes δ13C and δ15N.

The aim of this study was to compare the level of bone mass in digital orthopantomograms in two populations (medieval and current) using two radiomorphometric indexes, and to correlate the mandibular bone mass value, in the medieval mandible population, with stable isotope data δ13C and δ15N. An observational, cross-sectional, and analytical study on mandibles from two diachronic groups, 15 mandibles from the medieval settlement of La Torrecilla (Granada, Spain) and 15 mandibles from current patients at the Faculty of Dentistry of the University of Granada (Spain), matched by age and sex was conducted. The bone mass density was determined using the Mandibular Cortical Width Index (MCW) and the Mandibular Panoramic Index (PMI) in digital panoramic radiographs. In the medieval group, the values of bone mass density were correlated with those of two stable isotopes (δ13C and δ15N). The mean value of MCW in mm in the medieval group was 3.96 ± 0.60 (mean ± standard deviation) and in the current group was 4.02 ± 1.01. The PMI was 0.33 ± 0.06 and 0.35 ± 0.08 in the medieval and current groups respectively, with similar results in both groups (p = 0.820 and p = 0.575). A negative correlation was found between both morphometric indices and the δ15N isotope (rs = 0.56, p = 0.030 and rs = 0.61, p = 0.016, respectively). The bone mass density in mandibles belonging to the two compared populations, determined by two quantitative radiomorphometric indices, is similar. Within the medieval population, there is an inverse correlation between the δ15N value and bone mass density.

Assessment of the Absorbed Dose Variations in the Thyroid Gland Exposed to Orthopantomography (OPG) while Swallowing: A Novel Approach to Radiation Protection.

Background: The reliance on specialized diagnostic techniques is on the rise across various medical fields, including dentistry. While orthopantomogram (OPG), offers many advantages in terms of dental diagnosis, it also poses potential risks to sensitive organs, notably the thyroid gland. Objective: This study aimed to evaluate the fluctuations in the absorbed dose within the thyroid gland during swallowing while undergoing an OPG procedure. Material and Methods: In this computational simulation study, the BEAMnrc Monte Carlo code was employed to model an OPG machine, using 700 million particles across the energy range of 60-75 keV, which is standard for OPG procedures. The Monte Carlo (MC) model was cross-verified by comparing the derived spectra with those in the IPEM Report 78. A head and neck phantom was constructed using CT scan images with a slice thickness of 5 mm. This phantom underwent simulated beam exposure under two conditions: pre-swallow and post-swallow. Subsequently, the percentage depth dose was measured and contrasted across different depths. Results: After swallowing, there was an increase in the absorbed dose across all three regions of the thyroid (right, left, and center). Notably, regions near the hyoid bone exhibited a particularly significant increase in dose. In certain areas, the absorbed dose even tripled when compared to the pre-swallowing state. Conclusion: The findings indicate that during OPG imaging, swallowing can lead to an increased radiation dose to the thyroid gland. Given the thyroid's heightened sensitivity to radiation, such an increase in dosage is noteworthy.

Prevalence of impacted and transmigrated canines in orthodontic patients - A radiographic study.

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

Utilizing Deep Learning for Diagnosing Radicular Cysts.

OBJECTIVES: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs. MATERIALS AND METHODS: In this study, we conducted a comprehensive analysis of 138 radicular cysts and 100 normal panoramic radiographs collected from 2013 to 2023 at Clinical Hospital Dubrava. The images were annotated by a team comprising a radiologist and a maxillofacial surgeon, utilizing the GNU Image Manipulation Program. Furthermore, the dataset was enriched through the application of various augmentation techniques to improve its robustness. The evaluation of the algorithm's performance and a deep dive into its mechanics were achieved using performance metrics and EigenCAM maps. RESULTS: In the task of diagnosing radicular cysts, the initial algorithm performance-without the use of augmentation techniques-yielded the following scores: precision at 85.8%, recall at 66.7%, mean average precision (mAP)@50 threshold at 70.9%, and mAP@50-95 thresholds at 60.2%. The introduction of image augmentation techniques led to the precision of 74%, recall of 77.8%, mAP@50 threshold to 89.6%, and mAP@50-95 thresholds of 71.7, respectively. Also, the precision and recall were transformed into F1 scores to provide a balanced evaluation of model performance. The weighted function of these metrics determined the overall efficacy of our models. In our evaluation, non-augmented data achieved F1 scores of 0.750, while augmented data achieved slightly higher scores of 0.758. CONCLUSION: Our study underscores the pivotal role that deep learning is poised to play in the future of oral and maxillofacial radiology. Furthermore, the algorithm developed through this research demonstrates a capability to diagnose radicular cysts accurately, heralding a significant advancement in the field.

Oral Health Status and Treatment Needs Based on Artificial Intelligence (AI) Dental Panoramic Radiograph (DPR) Analysis: A Cross-Sectional Study.

Background: The application of artificial intelligence (AI) is gaining popularity in modern dentistry. AI has been successfully used to interpret dental panoramic radiographs (DPRs) and quickly screen large groups of patients. This cross-sectional study aimed to perform a population-based assessment of the oral health status and treatment needs of the residents of Kielce, Poland, and the surrounding area based on DPR analysis performed by a high-accuracy AI algorithm trained with over 250,000 radiographs. Methods: This study included adults who had a panoramic radiograph performed, regardless of indications. The following diagnoses were used for analysis: (1) dental caries, (2) missing tooth, (3) dental filling, (4) root canal filling, (5) endodontic lesion, (6) implant, (7) implant abutment crown, (8) pontic crown, (9) dental abutment crown, and (10) sound tooth. The study sample included 980 subjects. Results: The patients had an average of 15 sound teeth, with the domination of the lower dental arch over the upper one. The most commonly identified pathology was dental caries, which affected 99% of participants. A total of 67% of patients underwent root canal treatment. Every fifth endodontically treated tooth presented a periapical lesion. Of study group members, 82% lost at least one tooth. Pontics were identified more often (9%) than implants (2%) in replacing missing teeth. Conclusions: DPR assessment by AI has proven to be an efficient method for population analysis. Despite recent improvements in the oral health status of Polish residents, its level is still unsatisfactory and suggests the need to improve oral health. However, due to some limitations of this study, the results should be interpreted with caution.

Radiological Evaluation of the Accuracy of Demirjian, Nolla, and Willems Methods for Dental Age Estimation in 3-17-Year-Old Iranian Children.

Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t-test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years (P < 0.001) in girls and 0.09 years (P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years (P = 0.47) in girls and 0.12 years (P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years (P < 0.001) in girls and 0.40 years (P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.

Comparative analysis of clinical image evaluation charts for panoramic radiography.

OBJECTIVE: To compare and analyze professional (P chart) and simple (S chart) clinical image evaluation charts for evaluating panoramic radiograph image quality. METHODS: Ten 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. RESULTS: The 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. CONCLUSIONS: A 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.

DCM2Net: an improved face recognition model for panoramic stereoscopic videos.

The panoramic stereo video has brought a new visual experience for the audience with its immersion and stereo effect. In panoramic stereo video, the face is an important element. However, the face image in panoramic stereo video has varying degrees of deformation. This brings new challenges to face recognition. Therefore, this paper proposes a face recognition model DCM2Net (Deformable Convolution MobileFaceNet) for panoramic stereo video. The model mainly integrates the feature information between channels during feature fusion, redistributes the information between channels in the deeper part of the network, and fully uses the information between different channels for feature extraction. This paper also built a panoramic stereo video live system, using the DCM2Net model to recognize the face in panoramic stereo video, and the recognition results are displayed in the video. After experiments on different datasets, the results show that our model has better results on popular datasets and panoramic datasets.

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

OBJECTIVE: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images. METHODS: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models. RESULTS: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models). CONCLUSIONS: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA.

Micronuclei as an indicator of genotoxic change in epithelial cells of buccal mucosa after panoramic radiographs.

BACKGROUND: The radiation released at the time of dental panoramic radiographs causes genotoxic and cytotoxic effects on epithelial cells. OBJECTIVE: This research aimed to evaluate the changes in the frequencies of micronucleated cells in patients' buccal epithelial cells following dental panoramic radiography. METHODS: 74 patients were recruited for the study who were advised for panoramic radiographs. Using a wooden spatula, the buccal epithelial cells were scraped from both cheeks before to panoramic radiation exposure and ten days after the panoramic radiation exposure. Giemsa stain was used to stain the cells, and 500 cells were scored on a slide to determine the frequency of micronuclei. To determine the difference between the frequency of micronuclei before and after radiation exposure, a paired t-test was used in the statistical analysis. RESULTS: The proportion of micronuclei cells was 0.11% before radiation exposure and 0.57% following radiation exposure after 10 days. A statistically significant increase in the frequencies of micronuclei was noted after radiation exposure values. CONCLUSION: This study revealed the genotoxicity of epithelial cells with dental panoramic radiation exposure. It is advised to reduce the use of such radiographs and to use only when there is no other diagnostic tool that is helpful or when absolutely essential.

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

Is the third molar the most frequently extracted tooth? A population-based study utilizing dental panoramic radiographs in adults.

OBJECTIVES: The study aimed to examine the authenticity of the often-mentioned statement that the third molar is the most frequently extracted tooth. This finding has not been shown previously in a large population-based sample. MATERIALS AND METHODS: Data comprised a nationally representative sample of 6082 panoramic radiographs taken from adults in the cross-sectional Health 2000 Survey. From the radiographs, all missing teeth were recorded. Information on congenital agenesis of individual teeth was retrieved from two published meta-analyses. Primary outcome was the frequency of missing teeth by tooth type. Explanatory variables were age, sex, and the jaw (maxilla/mandible). Statistical analyses included χ2 test and binomial logistic regression. RESULTS: Mean age of participants (46% men, 54% women) was 53 years (SD 14.6; range 30‒97 years). Missing teeth occurred more often in women than in men (P < 0.001). The third molar was most frequently missing and the canine least frequently. In the maxilla and mandible, the third molar was missing more often than each of the other tooth types up to the age of 80 years (P < 0.01). CONCLUSIONS: When considering the rates of congenital agenesis of individual teeth, it is concluded that the third molar remained the most common tooth extracted up till the age of 80 years. CLINICAL RELEVANCE: The third molar is the most common target for extraction, but also the most common tooth associated with malpractice claims, and therefore, calls for skills, adequate equipment, and other resources for a successful extraction.

Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.

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

Evaluation of peri-implant bone defects on cone-beam computed tomography and the diagnostic accuracy of detecting these defects on panoramic images.

Purpose: This study was conducted to identify the typical sites and patterns of peri-implant bone defects on cone-beam computed tomography (CBCT) images, as well as to evaluate the detectability of the identified bone defects on panoramic images. Materials and Methods: The study population included 114 patients with a total of 367 implant fixtures. CBCT images were used to assess the presence or absence of bone defects around each implant fixture at the mesial, distal, buccal, and lingual sites. Based on the number of defect sites, the presentations of the peri-implant bone defects were categorized into 3 patterns: 1 site, 2 or 3 sites, and circumferential bone defects. Two observers independently evaluated the presence or absence of bone defects on panoramic images. The bone defect detection rate on these images was evaluated using receiver operating characteristic analysis. Results: Of the 367 implants studied, 167 (45.5%) had at least 1 site with a confirmed bone defect. The most common type of defect was circumferential, affecting 107 of the 167 implants (64.1%). Implants were most frequently placed in the mandibular molar region. The prevalence of bone defects was greatest in the maxillary premolar and mandibular molar regions. The highest kappa value was associated with the mandibular premolar region. Conclusion: The typical bone defect pattern observed was a circumferential defect surrounding the implant. The detection rate was generally higher in the molar region than in the anterior region. However, the capacity to detect partial bone defects using panoramic imaging was determined to be poor.

Beyond the mouth: Uncovering non-secretory multiple myeloma through oral symptoms.

Non-secretory multiple myeloma (NSMM) is a rare cancer of plasma cells characterized by the absence of detectable monoclonal M protein in the blood or urine. A 57-year-old woman presented with mandibular pain but without intraoral swelling. Imaging studies revealed multiple osteolytic lesions in her mandible and pronounced root resorption of the left mandibular second molar. Biopsy results showed atypical plasmacytoid cells positive for anti-kappa, CD138, MUM1, and CD79a antibodies, but negative for anti-lambda and CD20. These results were indicative of a malignant plasma cell neoplasm. No abnormalities were revealed by free light chain assay or by serum or urine protein electrophoresis, leading to a diagnosis of NSMM. The patient began chemotherapy in conjunction with bisphosphonate therapy and achieved remission following treatment. This case underscores the critical role of dentists in the early detection and prevention of NSMM complications, as the disease can initially present in the oral cavity.