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Objectives: Lingual mandibular bone depression (LMBD) is a developmental bony defect in the lingual aspect of the mandible that does not require any surgical treatment. It is sometimes confused with a cyst or another radiolucent pathologic lesion on panoramic radiography. Thus, it is important to differentiate LMBD from true pathological radiolucent lesions requiring treatment. This study aimed to develop a deep learning model for the fully automatic differential diagnosis of LMBD from true pathological radiolucent cysts or tumors on panoramic radiographs without a manual process and evaluate the model’s performance using a test dataset that reflected real clinical practice. Methods: A deep learning model using the EfficientDet algorithm was developed with training and validation data sets (443 images) consisting of 83 LMBD patients and 360 patients with true pathological radiolucent lesions. The test data set (1500 images) consisted of 8 LMBD patients, 53 patients with pathological radiolucent lesions, and 1439 healthy patients based on the clinical prevalence of these conditions in order to simulate real-world conditions, and the model was evaluated in terms of accuracy, sensitivity, and specificity using this test data set. Results: The model’s accuracy, sensitivity, and specificity were more than 99.8%, and only 10 out of 1500 test images were erroneously predicted. Conclusion: Excellent performance was found for the proposed model, in which the number of patients in each group was composed to reflect the prevalence in real-world clinical practice. The model can help dental clinicians make accurate diagnoses and avoid unnecessary examinations in real clinical settings.

Objective: We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography. Methods: Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (n = 100). Datasets with degraded image quality (n = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region. The images were distributed to the training and test datasets in a ratio of 9:1 for each group. The Pix2Pix GAN model was trained using pairs of the original and degraded image datasets for 100 epochs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were obtained for the test dataset, and two oral and maxillofacial radiologists rated the quality of clinical images. Results: Among the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups. Conclusion: The GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.

Objectives: This study aimed to analyze the quantitative fat fraction (FF) of the parotid gland in menopausal females with xerostomia using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) method. Methods: A total 138 parotid glands of 69 menopausal females were enrolled in our study and participants were divided into normal group and xerostomia group. The xerostomia group was divided into those with or without Sjögren’s syndrome. Participants underwent IDEAL-IQ sequences of MRI and the stimulated salivary flow test (s-SFR). The unpaired t-test was used to compare the FFs between the normal and xerostomia groups and between the subgroups with and without Sjögren’s syndrome. The correlation between FF and s-SFR was analyzed by Pearson’s correlation. Results: Excellent intra- and interobserver agreement during the measurement of FFs by IDEAL-IQ method (ICC>0.99, respectively). FF value in the xerostomia group was statistically significantly higher than the value in the normal group (p < 0.05). Within the xerostomia group, the average FF value of females with Sjögren’s syndrome was higher than that of females without Sjögren’s syndrome. However, the difference was not statistically significant (p > 0.05). Within the xerostomia group, FF value correlated negatively with s-SFR (p < 0.05). Conclusions: The FF of the parotid gland was higher in the xerostomia group than in the normal group and FF value and s-SFR showed a negative correlation. Analyses of the FF using IDEAL-IQ in menopausal females can be helpful for the quantitative diagnosis of xerostomia.

Quantifying physiological fat tissue in the organs is important to further assess the organ’s pathologic status. This study aimed to investigate the impact of body mass index (BMI), age, and sex on the fat fraction of normal parotid glands. Patients undergoing magnetic resonance imaging (MRI) of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) due to non-salivary gland-related disease were reviewed. Clinical information of individual patients was categorized into groups based on BMI (under/normal/overweight), age (age I/age II/age III), and sex (female/male) and an inter-group comparison of the fat fraction values of both parotid glands was conducted. Overall, in the 626 parotid glands analyzed, the fat fraction of the gland was 35.80%. The mean fat fraction value increased with BMI (30.23%, 35.74%, and 46.61% in the underweight, normal and overweight groups, respectively [p

Objective: This study aimed to identify robust radiomic features in multiultrasonography of the submandibular gland and normalize the interdevice discrepancies by applying a machine-learning-based harmonization method. Methods: Ultrasonographic images of normal submandibular gland of young healthy adults, aged between 20 and 40 years, were selected from two different devices. In a total of 30 images, the region of interest was determined along the border of gland parenchyma, and 103 radiomic features were extracted using A-VIEW. The coefficient of variation (CV) was obtained for individual features, and the features showing CV less than 10% were selected. For the selected features, the interdevice discrepancy was normalized using machine-learning method, called the ComBat harmonization. Median differences of the features between the two scanners, before and after harmonization, were compared using Mann–Whitney U-test; confidence interval of 95%. Results: Among total 103 radiomic features, 17 features were selected as robust, showing CV less than 10% in both scanners. All values of selected features, except two, showed a statistical difference between the two devices. After applying the ComBat harmonization method, the median and distribution of the 16 features were harmonized to show no significant difference between the two scanners (p > 0.05). One feature remained different (p ≤ 0.05). Conclusion: On ultrasonographic examination, robust radiomic features for normal submandibular gland were obtained and interdevice normalization was efficiently conducted using ComBat harmonization. Our findings would be useful for multidevices or multicenter studies based on clinical ultrasonographic imaging data to improve the accuracy of the overall diagnostic model.

OBJECTIVES This study aimed to investigate the anatomy of mandibular premolars from two perspectives-the canal configuration and radicular grooves-using cone-beam computed tomography (CBCT) in a large Korean population. MATERIALS AND METHODS CBCT images of mandibles acquired from March 2018 to December 2019 for dental treatment were randomly selected. In each image, the root canal of premolars was classified into 8 types according to the canal merging or diverging pattern and the number of apical foramens. The presence and the location of radicular grooves were also assessed. Statistical analysis was performed. RESULTS A total of 1463 first and 1448 s premolars from 732 patients (390 males, 342 females, mean age of 36.1 years) were evaluated. A single canal with one foramen predominated in both first (85.7%) and second (99.5%) premolars, while complete or partial multi-canals accounted for 14.3% and 0.5% of first and second premolars, respectively. The prevalence of radicular grooves was significantly higher in first premolars (13.2%) than in second premolars (0.5%) and in males (4.3%, n = 119) than in females (2.5%, n = 73). CONCLUSIONS Although most premolars were complete single canals, the first premolars showed a relatively higher number of complex canals compared to the second premolar. In addition, radicular grooves in mandibular premolars were significantly more common in male patients. CLINICAL RELEVANCE When planning the endodontic treatment of mandibular premolars, clinicians should be aware of their morphologic complexity, especially in the first premolar of male patients.

Background The purpose of this study was to analyze cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI) findings in temporomandibular joint disorder (TMD) patients and to comprehensively assess the relationships between these imaging findings and clinical symptoms. Methods A total of 754 temporomandibular joints (TMJs) in 377 patients with clinical symptoms of TMD who underwent both CBCT and MRI examinations were retrospectively reviewed. Clinical symptoms included TMJ pain, TMJ sound, and limitation of mouth opening. Oral radiologists evaluated osseous changes of the condylar head on CBCT, as well as the disc configuration, internal derangement, and joint effusion on MRI. The frequency of CBCT and MRI findings and the mean and standard deviation of age were analyzed. Logistic regression analysis was used to identify associations between these imaging findings and clinical symptoms using SPSS version 25.0 for Windows (IBM Corp., Armonk, NY, USA). The model fit was evaluated using the Hosmer and Lemeshow test. Results TMD patients consisted of 294 females and 83 males, and the age group of 20-39 accounted for 47.2% of the patients. Normal findings regarding osseous changes of the condylar head on CBCT were found in 65.1% of the patients. On MRI, a change in disc configuration was found in 54.9% of the patients, internal derangement in 62.6%, and joint effusion in 46.0%. TMJ pain was significantly associated with sclerosis [odds ratio (OR): 3.81], disc displacement without reduction (DDWOR) (OR: 3.22), grade 2 joint effusion (OR: 2.33), and grade 3 joint effusion (OR: 5.54). TMJ sound was significantly associated with disc displacement with reduction (DDWR) (OR: 3.04), DDWOR (OR: 2.50), grade 2 joint effusion (OR: 2.37), and grade 3 joint effusion (OR: 3.23). Limitation of mouth opening was significantly associated with flattened disc configuration (OR: 2.08), folded disc configuration (OR: 2.30), and grade 3 joint effusion (OR: 2.85). Conclusions CBCT findings had little to do with clinical symptoms. In contrast, MRI findings, including disc configuration, internal derangement, and joint effusion, were associated with clinical symptoms. These results suggest that MRI should be recommended over CBCT for the proper diagnosis of TMD patients.

This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol.From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient.The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion.The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) data set. Methods: In total, 2760 DPRs from 746 subjects who had 2–17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test data set included the latest DPR of each subject (746 images) and the other DPRs (2014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, -3, and -5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)-applied images. Results: This model had rank-1, -3, and -5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 s per epoch, and the prediction time for 746 test DPRs was short (3.2 s/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age range, 3–13 years) with mesiodens were used as a training and validation dataset. Deep learning models based on the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms for detecting mesiodens were developed, and each model was trained 300 times using training (540 images) and validation datasets (60 images). The performance of each model was evaluated based on accuracy, sensitivity, and specificity using 120 test images (60 periapical radiographs with mesiodens and 60 periapical radiographs without mesiodens). The accuracy of the YOLOv3, RetinaNet, and EfficientDet-D3 models was 97.5%, 98.3%, and 99.2%, respectively. The sensitivity was 100% for both the YOLOv3 and RetinaNet models and 98.3% for the EfficientDet-D3 model. The specificity was 100%, 96.7%, and 95.0% for the EfficientDet-D3, RetinaNet, and YOLOv3 models, respectively. The proposed models using three deep learning algorithms to detect mesiodens on periapical radiographs showed good performance. The EfficientDet-D3 model showed the highest accuracy for detecting mesiodens on periapical radiographs.

The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatment planning. This study proposes a deep learning-based method for fully automatic segmentation of the maxillary sinus, including clear or hazy states, on cone-beam computed tomographic (CBCT) images. A model for segmentation of the maxillary sinuses was developed using U-Net, a convolutional neural network, and a total of 19,350 CBCT images were used from 90 maxillary sinuses (34 clear sinuses, 56 hazy sinuses). Post-processing to eliminate prediction errors of the U-Net segmentation results increased the accuracy. The average prediction results of U-Net were a dice similarity coefficient (DSC) of 0.9090 ± 0.1921 and a Hausdorff distance (HD) of 2.7013 ± 4.6154. After post-processing, the average results improved to a DSC of 0.9099 ± 0.1914 and an HD of 2.1470 ± 2.2790. The proposed deep learning model with post-processing showed good performance for clear and hazy maxillary sinus segmentation. This model has the potential to help dental clinicians with maxillary sinus segmentation, yielding equivalent accuracy in a variety of cases.

Background As the application of cone-beam computed tomography (CBCT) in head and neck area increases for dental treatment purposes, cosmetic filler materials are incidentally observed. Since the materials are very diverse, unnecessary referrals or additional examination may be performed when clinicians are unfamiliar with the imaging findings. Thus, this study aimed to introduce the imaging characteristics of cosmetic fillers and grafts shown in dental CBCT with dental considerations that the clinician should be aware of. Methods CBCT obtained for dental purpose presenting cosmetic material were selected. The location of the material was identified as buccal, retroantral, parotid space, nose, zygoma, and symphysis. The material was classified as single or multiple, and grouped according to morphology: speckle, round, eggshell, linear, and amorphous. The radiopacity was classified as similar to soft tissue, between soft and hard tissue, similar to hard tissue, and metal. Results Twenty-one patients were reviewed, and all patients were female with mean age of 50.5 years. The buccal space was the most frequent location for multiple filler materials. The symphysis was the next frequent location and only single material were shown in this location. Cases having multiple filler showed diverse shapes while all single materials showed round shape. Fillers showing radiopacity of hard tissue were similar to diseases producing soft tissue calcifications. Metal-density material distributed in spaces induced white and dark streak artifacts in the CBCT image. All single materials presented radiopacity between soft and hard tissue and attached to the bone surface causing mandibular bone resorption. Conclusions Cosmetic materials displayed various imaging features in CBCT acquired during dental procedure. Clinicians should consider that cosmetic material may cause mandibular bone resorption and imaging artifacts on CBCT. Knowledge of the imaging characteristics of cosmetic fillers may help correct diagnosis.

Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.

This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

This study aimed to compare the therapeutic effects of corticosteroid irrigations and normal saline irrigations in the early inflammatory state of the salivary gland.Adult male Wistar rats were divided into experimental (n=6) and control (n=3) groups. Inflammation was induced in the experimental subjects on both sides of the submandibular gland with ligation. After 14 days, both sides of the glands were de-ligated and retroductal irrigation using saline (n=3) and a corticosteroid (n=3) was performed on the left sides only. The controls (n=3) were used to normalize the gland state for the effects of diet and aging. Magnetic resonance imaging was performed to confirm inflammation and post-irrigation gland recovery by measuring relative signal intensity (SI). The glands were excised for histological examination.All experimental animals showed inflamed glands with increased SI and subsequent recovery of the gland with decreased SI to varying degrees. The SI of the controls showed no significant changes during the overall period. The mean SI change of the irrigated gland was higher than that of the non-irrigated side, without a significant difference. The corticosteroid-irrigated glands showed a greater change in SI than that of the saline-irrigated glands. Histology revealed that inflammation was not observed in most of the irrigated glands, while mild to moderate quantities inflammatory cells were found in non-irrigated glands.Corticosteroid irrigation mitigated the early stages of salivary gland inflammation more effectively than normal saline.

BACKGROUND: Temporomandibular joint disorder (TMD), which is a broad category encompassing disc displacement, is a common condition with an increasing prevalence. This study aimed to develop an automated movement tracing algorithm for mouth opening and closing videos, and to quantitatively analyze the relationship between the results obtained using this developed system and disc position on magnetic resonance imaging (MRI). METHODS: Mouth opening and closing videos were obtained with a digital camera from 91 subjects, who underwent MRI. Before video acquisition, an 8.0-mm-diameter circular sticker was attached to the center of the subject’s upper and lower lips. The automated mouth opening tracing system based on computer vision was developed in two parts: (I) automated landmark detection of the upper and lower lips in acquired videos, and (II) graphical presentation of the tracing results for detected landmarks and an automatically calculated graph height (mouth opening length) and width (sideways values). The graph paths were divided into three types: straight, sideways-skewed, and limited-straight line graphs. All traced results were evaluated according to disc position groups determined using MRI. Graph height and width were compared between groups using analysis of variance (SPSS version 25.0; IBM Corp., Armonk, NY, USA). RESULTS: Subjects with a normal disc position predominantly (85.72%) showed straight line graphs. The other two types (sideways-skewed or limited-straight line graphs) were found in 85.0% and 89.47% in the anterior disc displacement with reduction group and in the anterior disc displacement without reduction group, respectively, reflecting a statistically significant correlation (χ(2)=38.113, P

This study investigated whether metal artifact reduction (MAR) applied computed tomography (CT) scans could be used to generate precise digital models and explored possible correlations between the amount of metal artifact and model accuracy. Thirty maxillofacial CT scans were randomly selected and a MAR algorithm was applied. By subtracting the original and MAR-applied CT images, the amount of metal artifact was quantified. Digital models were generated from the original and the MAR-applied CT data. Paired digital models were superimposed and shape deviation in planar surface was measured at 10 points in 4 planes. Statistical analyses were performed to compare deviations and to assess correlations between the amount of artifact and deviation. The MAR algorithm reduced metal artifact in all cases. The overall mean deviation of the MAR-applied models was 0.0868 mm, with no significant difference according to the reference plane. The amount of artifact did not significantly influence the accuracy of the digital models. MAR-applied CT is a convenient source for digital modeling with clinically acceptable accuracy. The MAR algorithm can be used regardless of the amount of metal artifact, which are generated by dental prostheses, for the quick and convenient manipulation of dental digital models.

The purpose of this study was to analyze the anatomical structures relevant for endodontic microsurgery in the mandibular posterior teeth using a cone-beam computed tomography (CBCT). A total of 963 mandibular posterior teeth were analyzed in CBCT scans from 133 patients. The buccolingual and mesiodistal dimensions of the root and the buccal bone thickness overlying the root were measured at the site of root resection (apical 3 mm). At this location, the relationship between the buccal cortical bone and root was classified into three types (separated, contact, and exposed), and the distance from the root apex to the mandibular canal was measured. The thickest buccolingual dimension of the roots was found in the mesial roots of first molars, at 5.59 ± 0.97 mm. The buccal bone thickness overlying the root became thicker in posterior tooth locations. In the first premolar and first molar mesial root, contact was the most common type of relationship between the buccal cortical bone and root. As the position of the teeth became more posterior, the distance from the apex to the mandibular canal became shorter. As the position of the teeth became more posterior, the buccal bone thickness increased and the distance to the mandibular canal became closer; therefore, particular attention is required for posterior teeth. The first premolar and the first molar mesial root are often in contact with the buccal cortical bone, which may allow infections to spread to the buccal structure more easily and negatively affect for post-surgical healing. When planning and performing endodontic microsurgery, understanding the anatomical structure of the surgical site will help minimize tissue damage and reduce complications.

Objective: The aim of this study is to introduce a novel program of panoramic radiography that shows 41 multilayer images from the buccal to lingual aspects in a region of interest, and to evaluate the effectiveness of this program for detecting proximal caries. Methods: In total, 480 premolars and molars on 30 panoramic radiographs taken with the multilayer imaging program were included in this study. The presence or absence of caries in 960 proximal surfaces was assessed by three experienced oral and maxillofacial radiologists as a consensus-based gold-standard. Two general dentists evaluated and scored proximal caries on 980 surfaces on panoramic radiographs with and without the multilayer imaging program. The two general dentists’ scores were compared with the gold-standard, and were analyzed for sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic (ROC) curve with and without using the program. The area under the ROC curve was analyzed using STATA/SE 13.1. Results: When the multilayer imaging program was used for panoramic radiography, the inter- and intraobserver agreement of the two general dentists improved. All values, including the area under the ROC curve, were higher when the multilayer imaging program was used than when it was not used. The area under the ROC curve showed a statistically significant improvement only in Observer 1, whose diagnostic ability was poorer than that of Observer 2. Conclusions:o This multilayer imaging program might help the inexperienced dentist to improve the diagnostic accuracy of proximal caries. If further studies would be performed in various clinical application, it could be useful when intraoral radiography taking is difficult for reasons such as mouth-opening limitations and the gag reflex of the patients.

Prognosis prediction of squamous cell carcinoma (SCC) with mandibular invasion is controversial, and a more sophisticated staging system to aid prognosis could be developed with imaging characteristics of bone invasion. Imaging-feature analysis provides practical, stratified results for survival prognosis in oral SCC (OSCC) of the mandible, and imaging advances enable more detailed tumor visualization. We retrospectively evaluated significant bone-invasion features associated with poor outcomes in mandibular OSCC to assess the predictive value of staging criteria that combined imaging features and histologic grade (combined imaging&ndash, histology (IH) grade) in 65 patients (39 men, 26 women) with mandibular SCC diagnosed from 2006 to 2016. Clinicopathologic features, including T-stage and histologic grade, and prognosis were retrieved. Tumors were classified into three types by extent of mandibular invasion on pretreatment imaging studies. Moreover, we assessed the involvement of the mandibular canal. We examined the correlation of factors associated with locoregional recurrence and overall mortality. The Harrell Concordance Index (C-index) determined prognostic performance of predictors. Nineteen (29%) patients showed locoregional recurrence and 13 (20%) died. For locoregional recurrence and mortality rates, imaging-detected mandibular canal (MC) involvement is a stronger prognostic factor for recurrence (C-index = 0.61 &gt, 0.58) and survival (C-index = 0.58 &gt, 0.63) than histopathologically confirmed perineural invasion, as was the IH grade, especially IH Grade 3, which was significantly associated with worse locoregional recurrence (p &lt, 0.02). Imaging-based staging showed higher prognostic performance than T-staging (C-index = 0.57 (recurrence), 0.60 (death)), when combined with histologic grading (C-index = 0.69 for both) or used alone (C-index = 0.63 (locoregional recurrence), 0.69 (death)). Overall survival was significantly stratified by Imaging type and IH grade. Therefore, analysis of imaging features provided more specific, practical results for survival prognosis in mandibular OSCC. Imaging advances can potentially provide detailed gross views of tumor masses to facilitate development of prognostic criteria for OSCC.

Objective The aim of this study was to analyze risk factors and establish a prediction model for temporomandibular joint (TMJ) disk perforation by constructing a nomogram. Study Design The study included a total of 282 joints in 274 patients. All patients underwent open TMJ surgery after obtaining magnetic resonance imaging (MRI), from 2005 to 2015. The presence or absence of disk perforation was confirmed during the operation. Patients were classified into 2 groups: perforation and nonperforation groups. We investigated demographic data and the characteristics of the disk, joint space, and bone on MRI. A logistic regression analysis was performed to analyze risk factors. A nomogram was constructed and validated internally and externally. Results Risk factors for disk perforation were increased age, disk shape (eyeglass or amorphous), low bone marrow signal, abnormal joint space, and 2 or more bony changes in the condyle and fossa. The area under the receiver operating characteristic curve of the nomogram was 0.908 (95% confidence interval [CI] 0.869-0.946) in the internal validation and 0.889 (95% CI 0.804-0.973) in the external validation with good suitability. Conclusions We were able to predict the probability of disk perforation with analyzed risk factors and constructed a nomogram, which may be helpful in proper diagnosis and treatment.

This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures.Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy.When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy.Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

Although the effects of osteoporosis on the skeleton are well studied, site-specific and long-term studies on the mandible are still lacking. This study investigated the time-course changes of the bone microarchitecture in the mandibular condyle in comparison to the corresponding changes in the alveolar bone, body of the mandible, and femur. Thirty-six 11-week-old female Sprague-Dawley rats were divided into ovariectomized (OVX) (24 rats) and sham (12 rats) groups. The right femur and mandible were obtained from 6 OVX rats and 3 sham rats at 8, 12, 26, and 36 weeks after surgery, respectively. The histomorphometric analysis was performed using micro–computed tomography and histologic assessments from the (1) distal femur; (2) the alveolar bone and (3) the body of the mandible; (4) the subchondral and (5) the central region of the condyle. The Brown-Forsythe test was used to verify the assumptions for statistical analysis, and the Mann-Whitney U test was then performed. The mandibular condyle showed increased trabecular bone in both the OVX and sham groups, while the bone density was reduced in the distal femur and the mandible interradicular septum and body. When comparing the OVX group to the sham group, only the central condyle showed a significant reduction in bone density at 36 weeks. Osteoporosis behaves in different manners in different parts of the skeleton, and clinicians should be aware that patients displaying osteoporotic changes in the mandible are expected to show severely advanced bone mineral density reduction in other bones, such as the femur.

Objective: This study investigated the usefulness of quantitative parameters [longitudinal relaxation (T1), transverse relaxation (T2), and proton density (PD)] obtained with synthetic magnetic resonance imaging (MRI) in assessing the progression of temporomandibular joint (TMJ) disorders. Methods: For individual TMJ disorder diagnoses, the presence of disc displacement in MRI and the osseous change in cone-beam CT were investigated. Joints were classified into three stages: (1) silent stage, no disc displacement or osseous change; (2) incipient stage, presence of disc displacement and absence of osseous change; and (3) progressed stage, both disc displacement and osseous change. In synthetic MRI, the T1, T2, and PD values of the condyle bone marrow were measured simultaneously. The median T1, T2, and PD values were analyzed according to disc displacement, osseous changes, and joint stage. Results: Significant differences were observed in the T1 and PD values of joints with disc displacement or condylar osseous change compared to normal joints. The T1 and PD values also differed between the silent and progressed stages. The PD value differed between the silent and incipient groups, while the T2 value did not differ significantly among the three groups. Conclusion: The PD and T1 values of condylar bone marrow obtained from synthetic MRI can be used as sensitive indicators of TMJ disorder progression. The PD value of the bone marrow showed potential as a useful biomarker for recognizing the initial stages of TMJ disorders. Synthetic MRI is useful for the simultaneous acquisition of effective MRI parameters for evaluating TMJ disorders.

Objective Both an elevated leukocyte count and periodontitis share well-recognized associations with cardiometabolic diseases. This cross-sectional study aimed to identify whether the leukocyte count is associated with periodontitis in a nationally representative Korean adult population. Materials and Methods Data from 9391 participants (3659 males and 5732 females) enrolled in 2012–2014 Korean National Health and Nutrition Examination Survey were analyzed. Leukocyte quartiles were categorized as follows: 3000 ≤ Q1 ≤ 4870, 4880 ≤ Q2 ≤5790, 5800 ≤ Q3 ≤ 6840, and 6850 ≤ Q4 ≤ 10000 cells/μl. Periodontitis was defined as scoring greater than or equal to ‘code 3’ in at least one site according to the WHO's Community Periodontal Index. The odds ratios (ORs) and 95% confidence intervals (95% CIs) for periodontitis in each leukocyte count quartile were calculated using multiple logistic regression analyses. Results The prevalence of periodontitis was directly correlated with increasing leukocyte quartiles: 19%, 20.4%, 24.3%, and 30.3%. Compared with the lowest leukocyte quartile group, the OR (95% CI) for periodontitis of the highest leukocyte quartile was 1.558 (1.285–1.891) after controlling for confounding factors. Conclusion An elevated leukocyte count was positively associated with the presence of periodontitis.

Purpose The aim of this study was to evaluate the computed tomography (CT) imaging findings and clinical symptoms of patients who complained of neurosensory disturbances after mandibular implant surgery, and to investigate the relationships of these parameters with the prognosis for recovery. Methods CT scans were reviewed in 56 patients with nerve disturbance after mandibular implant surgery. Two oral radiologists classified the imaging findings into intrusion, contact, close, and separate groups according to the distance from the inferior border of the implant to the roof of the mandibular canal (MC). The symptoms of 56 patients were classified into 8 groups and the frequency of each group was investigated. Patients were categorized according to symptom improvement into no recovery and recovery groups, and the relationships of recovery with the CT classification and specific symptom groups were analyzed. Results Thirty-eight of the 56 nerve disturbance cases showed improvement. The close and separate groups in the CT classification had a strong tendency for recovery (90.9% and 81.8%, respectively) (P, Graphical Abstract

Objective: This study was conducted to assess the clinical usability of the zero-echo time (ZTE) technique of MRI for evaluating bone changes of the temporomandibular joint (TMJ) in comparison with CBCT. Methods: Twenty patients with TMJ disorder who underwent both CBCT and MRI were randomly selected. CBCT images were obtained with an Alphard 3030 device (Asahi Roentgen Ind., Co. Ltd, Kyoto, Japan). MRIs were obtained using a 3.0 T scanner (Pioneer; GE Healthcare, Waukesha, WI, USA) and a 21-channel head coil. An isotropic three-dimensional proton-density-weighted ZTE sequence was acquired. Two radiologists evaluated 40 joints of 20 patients for the presence of the following osseous changes: flattening, erosion, osteophyte and sclerosis of the condyle; and flattening, erosion and sclerosis of the articular fossa. CBCT and ZTE-MRI assessments were performed at a 2-month interval. The prevalence-adjusted and bias-adjusted κ statistic was used to analyse interexaminer and intraexaminer agreement and the agreement between ZTE-MRI and CBCT. Results: Intraexaminer and interexaminer agreement analyses of ZTE-MRI showed high reproducibility (κ>0.80), which was comparable to that of CBCT. Flattening, osteophyte and sclerosis of the condyle and all types of bone changes in the mandibular fossa showed nearly perfect agreement between CBCT and ZTE-MRI (κ = 0.80–0.90). Erosion of the condyle showed substantial agreement between both sets of images (κ = 0.65–0.70). Conclusions: It is suggested that ZTE-MRI provides clinically reliable images for bone assessment in TMJ disorder. MRI may become a beneficial diagnostic tool for patients with both TMJ disc and bone pathology, with advantages involving medical costs and radiation dose.

Purpose The aim of this study was to investigate the available alveolar bone height between the maxillary molars and the sinus floor according to their anatomical relationship using cone-beam computed tomographic (CBCT) images. Methods A total of 752 maxillary first (M1) and second molars (M2) on CBCT scans of 188 patients were selected. First, each maxillary molar was categorized as type 1, 2, 3, or 4 according to the relationship of the molar root with the maxillary sinus floor. The frequency distribution of each type was analyzed. Second, the shortest vertical distance (VD) of each molar was measured from the furcation midpoints of the roots to the lowest point of the sinus floor by 2 observers. Intraclass correlation coefficients and the t-test were calculated for the VD measurements. Results For M1, type 3 was the most frequent, followed by type 2. For M2, type 3 was the most common, followed by type 1. The VD measurements of type 1 were 9.51±3.68 mm and 8.07±2.73 mm for M1 and M2, and those of type 3 were 3.70±1.52 mm and 4.03±1.53 mm for M1 and M2, respectively. The VD measurements of M2 were significantly higher in female patients than in male patients. Conclusions Type 3 was the most frequent anatomical relationship in the maxillary molars, and showed the lowest alveolar bone height. This information will help clinicians to prevent complications related to the maxillary sinus during maxillary molar treatment and to predict the available bone height for immediate implant planning., Graphical Abstract

Objectives: The purpose of this retrospective study is to find the differentiating characteristics of cystic and cystic-appearing lesions that involve the impacted mandibular third molar by analyzing panoramic radiographs and computed tomography images, and to aid the preoperative diagnosis. Materials and Methods: Eighty-one patients who had a mandibular cystic or cystic-appearing lesion that involved impacted mandibular third molar and underwent cyst enucleation were included in the study. The preoperative panoramic radiograph and computed tomography findings were analyzed in accordance to the histopathologic type. Results: Most of the cystic lesions containing the mandibular third molar were diagnosed as a dentigerous cyst (77.8%). The occurrence of mesio-distal displacement of the third molar was more frequent in the odontogenic keratocyst (71.4%) and in the ameloblastoma (85.7%) than in the dentigerous cyst (19.1%). Downward displacement was primarily observed in each group. Odontogenic keratocyst and ameloblastoma showed more aggressive growth pattern with higher rate of bony discontinuity and cortical bone expansion than in dentigerous cyst. Conclusion: When evaluating mandibular cystic lesions involving the impacted mandibular third molar, dentigerous cyst should first be suspected. However, when the third molar displacement and cortical bone absorption are observed, then odontogenic keratocyst or ameloblastoma should be considered.

Synovial chondromatosis is a rare benign lesion originating from the synovial membrane. It presents as adhesive or non-adhesive intra-articular cartilaginous loose bodies. Although the causes of synovial chondromatosis have not been fully elucidated, inflammation, external injury, or excessive use of joints have been suggested as possible causes. Synovial chondromatosis has been reported to occur most frequently at large joints that bear weights, with a rare occurrence at the temporomandibular joint (TMJ). When synovial chondromatosis develops at TMJ, clinical symptoms, including pain, joint sounds, and mouth opening may common. Moreover, synovial chondromatosis rarely spreads to the mandibular condyle, glenoid cavity, or articular eminence of TMJ. The goal of this study was to discuss the methods of surgery and other possible considerations by reviewing cases of patients who underwent surgery for synovial chondromatosis that extended to the temporal bone.

This study describes the use of personal computer (PC)-based three-dimensional computed tomographic (3D CT) images in the evaluation of supernumerary and ectopically impacted teeth. Three selected cases were presented as examples of the more complex cases in which 3D CT imaging added information not readily available from periapical, occlusal, or panoramic radiographs. Patients were CT scanned from the occlusal plane to the periapical region of the impacted teeth. Digital Image and Communications in Medicine CT data were transferred to a personal laptop computer using a network line. 3D volume rendering was performed using PC-based volumetric analysis software. 3D CT-reformatted imaging of the teeth is a useful way to investigate and localize supernumerary or impacted teeth. Newer software that enables this investigation using a PC provides a relatively inexpensive way to carry out such investigations, making it easier for dental practitioners to request such investigations and to view the results in real time in their own offices.

27세 여자 환자가 좌측 이하선 부위의 부종을 주소로 내 원하였다. 내원 6개월 전 좌측 교근 부위에 보톡스 시술을 받았으며, 주사 후 4개월 정도 지나서부터 좌측 안면부 종 창이 시작되었다. 초진 임상 검사시, 좌측 이하선 부위의 파동성 종창이 관찰되었다.(Fig. 1) 통증은 없었으며, 개구 제한은 관찰되지않았다. 자기공명 T2 강조 영상에서 좌측 이하선의 표층엽 내부 에 약 1.7×1.1 cm 크기의 고강도 신호의 낭성 병소가 관찰 되었으며, 병소는 대부분 고강도 신호를 보이나 내부에 이 질성 강도 신호를 보였다. T1 강조영상에서 병소는 대부분 저강도 신호를 보였으며 내부에 더욱 낮은 강도 신호의 병 소가 관찰되었다.(Fig. 2) 병소 주변의 임파선 종창 등의 소 견은 관찰되지 않았다. 초음파 영상에서는 약 3 cm 크기의 허 종 기 135-720 서울시강남구언주로 712 연세대학교강남세브란스병원구강악안면외과 Jong-Ki Huh Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University 712 Eonju-ro, Gangnam-gu, Seoul, 135-720, Korea TEL: +82-2-2019-3560 FAX: +82-2-3463-4052 E-mail: omshuh@yuhs.ac 이하선에서 발견된 유표피낭: 증례 보고