Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

3'4'7-Trihydroxyflavone inhibits RANKL-induced osteoclast formation via NFATc1.

3'4'7-Trihydroxyflavone is a flavonoid from ladino clover, alfalfa, and Albizzia julibrissin. In the present study, we found that 3'4'7-trihydroxyflavone markedly inhibited the receptor activator of nuclear factor kappa B ligand (RANKL) induced osteoclastic differentiation from mouse bone marrow derived macrophages (BMMs). 3'4'7-trihydroxyflavone also reduced the mRNA expression level of osteoclastic marker genes including calcitonin receptor (CTR), Cathepsin K1 v-ATPase V0 subunit d2 (ATP6v0d2), and dendritic cell-specific transmembrane protein (DC-STAMP). In addition, 3'4'7-trihydroxyflavone decreased the bone resorption activity of osteoclasts on dentin slices. We found that 3'4'7-trihydroxyflavone inhibited RANKL-induced expression of nuclear factor of activated T cells c1 (NFATc1), a key transcription factor of osteoclast differentiation. Furthermore, 3'4'7-trihydroxyflavone attenuated RANKL-induced activation of p38 mitogen-activated protein kinase (MAPK) and expression of B lymphocyte-induced maturation protein 1 (Blimp1), a repressor of negative regulators of NFATc1. Taken together, our data suggest that 3'4'7-trihydroxyflavone inhibits osteoclastogenesis via NFATc1.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

OBJECTIVES: This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS: PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS: The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS: This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN: A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS: The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS: The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Head and neck manifestations of fibrodysplasia ossificans progressiva: Clinical and imaging findings in 2 cases.

Fibrodysplasia ossificans progressiva is a rare hereditary disorder characterized by progressive heterotopic ossification in muscle and connective tissue, with few reported cases affecting the head and neck region. Although plain radiographic findings and computed tomography features have been well documented, limited reports exist on magnetic resonance findings. This report presents 2 cases of fibrodysplasia ossificans progressiva, one with limited mouth opening due to heterotopic ossification of the lateral pterygoid muscle and the other with restricted neck movement due to heterotopic ossification of the platysma muscle. Clinical findings of restricted mouth opening or limited neck movement, along with radiological findings of associated heterotopic ossification, should prompt consideration of fibrodysplasia ossificans progressiva in the differential diagnosis. Dentists should be particularly vigilant with patients diagnosed with fibrodysplasia ossificans progressiva to avoid exposure to diagnostic biopsy and invasive dental procedures.

Mucormycosis-related osteomyelitis of the maxilla in a post-COVID-19 patient.

Mucormycosis is a rare, invasive fungal infection that progresses aggressively and requires prompt surgery and appropriate treatment. The number of cases of mucormycosis in coronavirus disease 2019 (COVID-19) patients has recently increased, and patients with uncontrolled diabetes mellitus are particularly at an elevated risk of infection. This report presents a case of mucormycosis-related osteomyelitis of the maxilla in a 37-year-old man with diabetes mellitus. The patient complained of severe and persistent pain in the right maxilla, accompanied by increased tooth mobility and headache. On contrast-enhanced computed tomographic images, gas-forming osteomyelitis of the right maxilla was observed. Destruction of the maxilla and palatine bone then proceeded aggressively. Sequestrectomy was performed on the right maxilla, and the histopathological diagnosis was mucormycosis. Further investigation after the first operation revealed the patient's history of COVID-19 infection.

Deep learning-based apical lesion segmentation from panoramic radiographs.

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

Differentiation of osteosarcoma from osteomyelitis using microarchitectural analysis on panoramic radiographs.

Diagnosing osteosarcoma (OS) is very challenging and OS is often misdiagnosed as osteomyelitis (OM) due to the nonspecificity of its symptoms upon initial presentation. This study investigated the possibility of detecting OS-induced trabecular bone changes on panoramic radiographs and differentiating OS from OM by analyzing fractal dimensions (FDs) and degrees of anisotropy (DAs). Panoramic radiographs of patients with histopathologically proven OS and OM of the jaw were obtained. A total of 23 patients with OS and 40 patients with OM were enrolled. To investigate whether there was a microarchitectural difference between OS lesions and normal trabecular areas in each patient, two regions of interest (ROIs) were located on the CT images. Three microarchitectural parameters (box-counting FD, fast Fourier transform-based FD, and DA) were calculated. For both OS and OM, significant differences were found for all three microarchitectural parameters. Compared to normal trabecular bone, trabecular bone affected by OS and OM became isotropic and more complex. When comparing OS and OM, a statistically significant difference was found only in DA. Trabecular bones affected by OS became more isotropic than those affected by OM. Microarchitectural analysis, especially DA, could be useful for detecting OS-induced trabecular alterations and differentiating OS from OM.

In vitro toxicity assessment of crosslinking agents used in hyaluronic acid dermal filler.

Hyaluronic acid (HA) dermal fillers are produced by crosslinking HA with agents, such as 1,4-butanediol diglycidyl ether (BDDE) and poly (ethylene glycol) diglycidyl ether (PEGDE) to acquire desired properties. Thus, the safety evaluation of these crosslinkers is needed at the cellular level. In the present study, cell viability, cytotoxicity, membrane integrity, reactive oxygen species (ROS), mitochondrial membrane potential (MMP), and inflammatory responses were evaluated in the human keratinocyte cell line, HaCaT and human dermal fibroblast cell line, HDF in response to treatment with the crosslinkers. In both the cell lines, BDDE significantly decreased cell viability at 100-1000 ppm, while PEGDE showed a decrease at 500-1000 ppm. In HaCaT cells, BDDE markedly increased cytotoxicity (lactate dehydrogenase release) at 100-1000 ppm, but PEGDE showed an increase at 500-1000 ppm. Cells treated with BDDE (100 ppm) caused alteration in the integrity of cell membrane and shape. In both the cell lines, BDDE-treated cells showed significantly higher ROS levels and MMP loss than PEGDE-treated cells. Also, BDDE-treated cells exhibited higher COX-2 expression at 100 ppm. Expression of inflammatory cytokines (TNF-α, and IL-1 ß) was higher in BDDE-treated cells. Taken together, PEGDE-treated cells showed markedly lower cytotoxicity, ROS production, and inflammatory responses than BDDE-treated cells. Our data suggest that PEGDE is safer than BDDE as a crosslinker in HA dermal fillers.

Diagnostic considerations in central odontogenic fibroma of the maxilla: 2 case reports.

Central odontogenic fibroma (COF) is defined as a fibroblastic odontogenic tumor characterized by varying density of the tooth epithelium. It is an extremely rare benign neoplasm that occurs in the maxilla and the mandible; only a few reports of COF are available in the literature. Diagnosis of the lesion based only on the radiological features of COF is difficult due to variation in the findings regarding this condition. This report describes 2 clinical cases of middle-aged women with COF. Clinical examination revealed palatal mucosal depression; additionally, oral examination, as well as panoramic radiographs, intraoral radiographs, and computed tomography scans, revealed severe root resorption. This report highlights the clinical and radiological imaging features of COF, with the goal of enabling straightforward differential diagnosis of the lesion by the clinician and thereby appropriate treatment of the patient.

Steatocystoma multiplex: A case report of a rare entity.

Steatocystoma multiplex is an uncommon benign skin disease, which typically manifests as numerous intradermal cysts that can be scattered anywhere on the body. Although usually asymptomatic, it can be significantly disfiguring. One type of steatocystoma multiplex is known to be associated with the autosomal dominant inheritance of a mutation in the gene coding for keratin 17 (KRT17). In such cases, it is often concurrent with other developmental abnormalities of the ectoderm-derived tissues, such as the nails, hair, and teeth. To the best of our knowledge, few cases have been reported of steatocystoma multiplex of the oral and maxillofacial region. This report describes a case of steatocystoma multiplex of both sides of the neck and multiple dental anomalies, with a focus on its clinical, radiological, and histopathological characteristics, as well as the possibility that the patient exhibited the familial type of this condition.

Radiculomegaly of canines in oculofaciocardiodental syndrome.

Oculofaciocardiodental (OFCD) syndrome is a rare genetic disease, first reported by Hayward in 1980. This syndrome presents with various ocular, facial, cardiac, and dental symptoms, including congenital cataract, dysmorphic facial features, congenital heart disease, and enlarged roots, respectively. The most important criteria for the diagnosis of OFCD syndrome are dental abnormalities, especially extreme elongation of canine roots. Here, we report detailed analysis of the dentofacial region, as well as ocular, facial, cardiac, and dental findings in a female with OFCD syndrome. To the best of our knowledge, the patient in this case is the first such patient reported in South Korea.

Unusual malignant neoplasms occurring around dental implants: A report of 2 cases.

Osseointegrated implants are now commonplace in contemporary dentistry. However, a number of complications can occur around dental implants, including peri-implantitis, maxillary sinusitis, osteomyelitis, and neoplasms. There have been several reports of a malignant neoplasm occurring adjacent to a dental implant. In this report, we describe 2 such cases. One case was that of a 75-year-old man with no previous history of malignant disease who developed a solitary plasmacytoma around a dental implant in the left posterior mandible, and the other was that of a 43-year-old man who was diagnosed with squamous cell carcinoma adjacent to a dental implant in the right posterior mandible. Our experiences with these 2 cases suggest the possibility of a relationship between implant treatment and an inflammatory cofactor that might increase the risk of development of a malignant neoplasm.

Volumetric accuracy of cone-beam computed tomography.

PURPOSE: This study was performed to investigate the influence of object shape and distance from the center of the image on the volumetric accuracy of cone-beam computed tomography (CBCT) scans, according to different parameters of tube voltage and current. MATERIALS AND METHODS: Four geometric objects (cylinder, cube, pyramid, and hexagon) with predefined dimensions were fabricated. The objects consisted of Teflon-perfluoroalkoxy embedded in a hydrocolloid matrix (Dupli-Coe-Loid TM; GC America Inc., Alsip, IL, USA), encased in an acrylic resin cylinder assembly. An Alphard Vega Dental CT system (Asahi Roentgen Ind. Co., Ltd, Kyoto, Japan) was used to acquire CBCT images. OnDemand 3D (CyberMed Inc., Seoul, Korea) software was used for object segmentation and image analysis. The accuracy was expressed by the volume error (VE). The VE was calculated under 3 different exposure settings. The measured volumes of the objects were compared to the true volumes for statistical analysis. RESULTS: The mean VE ranged from -4.47% to 2.35%. There was no significant relationship between an object's shape and the VE. A significant correlation was found between the distance of the object to the center of the image and the VE. Tube voltage affected the volume measurements and the VE, but tube current did not. CONCLUSION: The evaluated CBCT device provided satisfactory volume measurements. To assess volume measurements, it might be sufficient to use serial scans with a high resolution, but a low dose. This information may provide useful guidance for assessing volume measurements.

Attenuation of RANKL-induced Osteoclast Formation via p38-mediated NFATc1 Signaling Pathways by Extract of Euphorbia Lathyris L.

BACKGROUND: Osteoclasts are the only cell type capable of breaking down bone matrix, and its excessive activation is responsible for the development of bone-destructive diseases. Euphorbia lathyris L. (ELL) is an herbal plant that belongs to the Euphorbiaceae family. This study investigated the effects of the methanol extract of the aerial part of ELL on receptor activator of nuclear factor-kappa B ligand (RANKL)-induced osteoclast formation and signaling pathways. METHODS: Osteoclasts were formed by co-culturing mouse bone marrow with osteoblasts or by culturing mouse bone marrow-derived macrophages (BMMs) with macrophage colony-stimulating factor (M-CSF) and RANKL. Bone resorption assays were performed using dentine slices. The expression level of mRNA was analyzed by real-time polymerase chain reaction (PCR) or reverse transcription (RT)-PCR. Western blotting assays were performed to detect the expression or activation level of proteins. RESULTS: ELL inhibited RANKL-induced osteoclast formation without cytotoxicity. Furthermore, the RANKL-stimulated bone resorption was diminished by ELL. Mechanistically, ELL blocked the RANKL-triggered p38 mitogen-activated protein kinase (MAPK) phosphorylation, which resulted in the suppression of the expression of c-Fos and nuclear factor of activated T cells (NFATc1). In osteoblasts, ELL had little effect on the mRNA expression of RANKL and osteoprotegerin (OPG). CONCLUSIONS: The present data suggest that ELL has an inhibitory effect on osteoclast differentiation and function via downregulation of the p38/c-Fos/NFATc1 signaling pathways. Thus, ELL could be useful for the treatment of bone diseases associated with excessive bone resorption.