Elevated Expression of Cathepsin K in Periodontal Ligament Fibroblast by Inflammatory Cytokines Accelerates Osteoclastogenesis via Paracrine Mechanism in Periodontal Disease.

Cathepsin K (CTSK) is a cysteine protease that is mainly produced from mature osteoclasts and contributes to the destruction of connective tissues and mineralized matrix as a consequence of periodontal disease (PD). However, few studies have reported its regulatory role in osteoclastogenesis-supporting cells in inflammatory conditions. Here, we investigated the role of CTSK in osteoclastogenesis-supporting cells, focusing on the modulation of paracrine function. Microarray data showed that CTSK was upregulated in PD patients compared with healthy individuals, which was further supported by immunohistochemistry and qPCR analyses performed with human gingival tissues. The expression of CTSK in the osteoclastogenesis-supporting cells, including dental pulp stem cells, gingival fibroblasts, and periodontal ligament fibroblasts (PDLFs) was significantly elevated by treatment with inflammatory cytokines such as TNFα and IL-1ß. Moreover, TNFα stimulation potentiated the PDLF-mediated osteoclastogenesis of bone marrow-derived macrophages. Interestingly, small interfering RNA-mediated silencing of CTSK in PDLF noticeably attenuated the TNFα-triggered upregulation of receptor activator of nuclear factor kappa-B ligand (RANKL), macrophage colony-stimulating factor, and RANKL/osteoprotegerin ratio, thereby abrogating the enhanced osteoclastogenesis-supporting activity of PDLF. Collectively, these results suggest a novel role of CTSK in the paracrine function of osteoclastogenesis-supporting cells in periodontal disease.

Differences in soft-tissue thickness changes after bimaxillary surgery between patients with vertically high angle and normal angle.

INTRODUCTION: We evaluated soft-tissue thickness changes after bimaxillary surgery according to vertical facial patterns in patients with skeletal Class III malocclusion with mandibular prognathism. METHODS: Forty-three Korean patients (16 men and 27 women; mean age, 22.6 ± 4.1 years) with skeletal Class III malocclusion who underwent bimaxillary surgery were divided into 2 groups: normal-angle group (N group) and high-angle group (H group), on the basis of the presurgical angle of the mandibular plane relative to the sella-nasion plane (SN-MP). Changes in hard-tissue landmarks and soft-tissue thickness before and after surgery were analyzed from reconstructed 3-dimensional cone-beam computed tomography images. Postoperative soft-tissue thickness in both groups was compared with that in 40 patients with normal skeletal Class I malocclusion in the reference group. RESULTS: Group N (27°-37°) and group H (>37°) did not differ significantly in terms of sex and age before surgery. Preoperative pogonion (Pog) thickness was significantly less in group H (9.7 ± 1.6 mm) than in group N (10.8 ± 1.9 mm) (P = 0.042). Adjusted multiple linear regression analysis showed a weak positive linear relationship between the SN-MP before surgery and soft-tissue Pog thickness change (R2 of 0.361; P = 0.001) after surgery, but the area below the lower lips was not completely normalized despite surgery. CONCLUSIONS: The thickness of the soft-tissue Pog may increase slightly after surgery in patients with skeletal Class III malocclusion with a higher preoperative mandibular plane angle, but normalization in the area cannot be completely achieved despite surgery.

Treatment of peri-invagination lesion and vitality preservation in an immature type III dens invaginatus: a case report.

BACKGROUND: To report a case of type III dens invaginatus associated with peri-invagination periodontitis in an immature permanent mandibular central incisor with open apex, in which only the invagination area was treated and vitality was preserved. CASE PRESENTATION: A 9-year-old boy was referred complaining of pain in the mandibular left central incisor. After radiographic examination, an invagination into the pulp chamber of the tooth associated with periapical radiolucency was detected. Endodontic access was performed and the orifice was identified under a dental operating microscope. The invagination area was chemo-mechanically cleaned. After 1 week, the invagination was obturated with mineral trioxide aggregate. During the 2-year follow up period, the tooth was asymptomatic. Radiographic examination revealed significant progression of periapical healing and root development in the main root canal of the tooth. CONCLUSION: Non-surgical root canal treatment of the invagination may preserve pulp vitality, and continuous root development of the tooth.

Accessory mental foramina associated with neurovascular bundle in Korean population.

PURPOSE: To determine the prevalence and anatomical features of accessory mental foramina (AMFs) associated with neurovascular bundle using reformatted CBCT images in Korean population. MATERIALS AND METHODS: Three-dimensional, cross-sectional, and axial images of CBCT from total 446 patients were evaluated. To include only AMF associated with neurovascular bundle, the course of accessory nerve through AMF was evaluated on three-dimensional images. The prevalence, locations to mental foramen (MF), and distribution to the adjacent tooth of AMF were analyzed. Additionally, the distance from MF and dimension of AMF were measured. RESULTS: AMFs were found in 36 (8.1 %) patients in the 446 Koreans. The prevalence rate of AMFs in male was significant higher than that in female (p < 0.05). Most AMFs were located anterior-superior to MF. The shortest distance between the center of AMF and that of MF was 5.80 mm (SD ± 2.90, 1.40-13.0). Variance of location of AMF in relation to MF was greater in the horizontal direction than in the vertical direction. In related to adjacent tooth, AMF was mainly distributed between the first and second premolars. The horizontal dimension and vertical dimension of AMF were 1.54 mm (SD ± 1.41, 0.70-9.80) and 1.44 mm (SD ± 0.41, 0.70-2.30), respectively. CONCLUSIONS: Knowledge of AMF is important for performing effective nerve block and avoiding injuries to neurovascular bundles passing through AMF. Evaluation of the course of AMF in three-dimensional image of CBCT is effective for confirming AMF associated with inferior alveolar nerve.

Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients.

Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric patients and to assess the accuracy. The study included patients aged 8-16 years who visited the Pusan National University Dental Hospital and underwent panoramic radiography. A total of 806 panoramic radiographs were retrospectively analyzed to determine the presence or absence of missing teeth for each tooth number. Moreover, each panoramic radiograph was divided into four quadrants, each of a smaller size, containing both primary and permanent teeth, generating 3224 data. Quadrants with missing teeth (n = 1457) were set as the experimental group, and quadrants without missing teeth (n = 1767) were set as the control group. The data were split into training and validation sets in a 4:1 ratio, and a 5-fold cross-validation was conducted. A gradient-weighted class activation map was used to visualize the deep learning model. The average values of sensitivity, specificity, accuracy, precision, recall and F1-score of this deep learning model were 0.635, 0.814, 0.738, 0.730, 0.732 and 0.731, respectively. In the experimental group, the accuracy was the highest for missing canines and premolars, and the lowest for molars. The deep learning model exhibited a moderate to good distinguishing power with a classification performance of 0.730. This deep learning model and the newly defined small sized region of interest proved adequate for classifying the presence of missing teeth.

Efficacy of Different Irrigation Needles and Ultrasonic Activation on Calcium Hydroxide Removal: A Micro-CT Study Using 3D-Printed Endodontic Models.

INTRODUCTION: The aim of this study was to evaluate the efficacy of different irrigation needles and passive ultrasonic activation in removing Ca(OH)2 from an endodontic model that duplicated a root canal configuration of a human natural tooth. METHODS: An extracted human maxillary premolar was subjected to root canal preparation and scanned with microcomputed tomography. A 3-dimensional reconstruction model of the natural tooth was printed to endodontic models using a polyjet printer. The root canals of the models were filled with Ca(OH)2 paste and divided into 2 groups based on the irrigation protocol: conventional syringe-needle irrigation (conventional group) and passive ultrasonic irrigation (PUI) group. Each group was subdivided into 3 groups (n = 10) according to the type of needle: half-cut, side-vented, and TruNatomy irrigation needle. Microcomputed tomographyimaging was used to assess the percentage of reduction of Ca(OH)2. Data were analyzed using two-way analysis of variance test (α = .05). RESULTS: The side-vented and TruNatomy irrigation needles showed significantly higher percentage reductions than the half-cut needle (P < .05) in the conventional irrigation group. The PUI group showed significantly higher percentage reductions of Ca(OH)2 than the conventional group regardless of the type of needle (P < .05). However, no significant difference was found among the needles in the PUI group. CONCLUSIONS: The type of irrigation needle and the use of PUI influenced the removal efficacy of Ca(OH)2. PUI enhanced the removal of Ca(OH)2 regardless of the type of irrigation needle.

Three-dimensional soft tissue landmark detection with marching cube algorithm.

Current method of analyzing three-dimensional soft tissue data, especially in the frontal view, is subjective and has poor reliability. To overcome this limitation, the present study aimed to introduce a new method of analyzing soft tissue data reconstructed by marching cube algorithm (Program S) and compare it with a commercially available program (Program A). Cone-beam computed tomography images of 42 patients were included. Two orthodontists digitized six landmarks (pronasale, columella, upper and lower lip, right and left cheek) twice using both programs in two-week intervals, and the reliability was compared. Furthermore, computer-calculated point (CC point) was developed to evaluate whether human error could be reduced. The results showed that the intra- and inter-examiner reliability of Program S (99.7-100% and 99.9-100%, respectively) were higher than that of Program A (64.0-99.9% and 76.1-99.9%, respectively). Moreover, the inter-examiner difference of coordinate values and distances for all six landmarks in Program S was lower than Program A. Lastly, CC point was provided as a consistent single point. Therefore, it was validated that this new methodology can increase the intra- and inter-examiner reliability of soft tissue landmark digitation and CC point can be used as a landmark to reduce human error.

Aggressive central odontogenic fibroma in the maxilla: A case report.

A central odontogenic fibroma is a rare benign tumor composed of mature fibrous connective tissue with variable amounts of odontogenic epithelium. It appears at similar rates in the maxilla and mandible. In the maxilla, it usually occurs anterior to the molars. Radiographically, central odontogenic fibroma commonly presents as a multilocular or unilocular radiolucency with a distinct border. This paper reports a case of an aggressive central odontogenic fibroma involving the right posterior maxilla of a 53-year-old man. Radiographs showed an extensive soft tissue mass involving the entire right maxilla with frank bone resorption. The patient had a history of 2 operations in the region, both more than 2 decades ago. Although it was impossible to confirm the previous diagnoses, it was presumed that this case was a recurrent lesion.

Analysis of the root position and angulation of maxillary premolars in alveolar bone using cone-beam computed tomography.

Purpose: This study investigated whether the relationship between the maxillary sinus and the root of the maxillary premolar is correlated with the root position and whether there is a difference in the long axis angle of premolars and the buccal bone thickness according to the sinus-root relationship and root position. Materials and Methods: Cone-beam computed tomographic images of 587 maxillary first premolars and 580 second premolars from 303 patients were retrospectively reviewed. The maxillary sinus floor-root relationship was classified into 4 types, and the root position in the alveolar bone was evaluated as buccal, middle, or palatal. The long axis angle of the maxillary premolars in the alveolar bone and the buccal bone thickness were measured. The correlation between these parameters was analyzed. Results: The maxillary sinus floor-root relationship showed a statistically significant correlation with the root position in the alveolar bone. Most maxillary first premolars were buccally located, and more than half of the second premolars had their roots in the middle. The long axis angle of the premolars was significantly larger in buccal-positioned teeth than in middle-positioned teeth, and the buccal bone was thinner. Conclusion: When the root of the maxillary premolar was separated from the sinus floor, the premolar was often located on the buccal side. Most of the maxillary first premolars had a thinner buccal bone and larger inclination than the second premolars. It is recommended to evaluate the root position, sagittal angle and buccal bone thickness using CBCT for implant treatment planning.

Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children.

OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology. METHODS: Out of 988 panoramic radiographs, 489 patients with mesiodens were classified as an experimental group, and 499 patients without mesiodens were classified as a control group. This study consists of two networks. The first network (DeeplabV3plus) is a segmentation model that uses the posterior molar space to set the ROI in the maxillary anterior region with the mesiodens in the panoramic radiograph. The second network (Inception-resnet-v2) is a classification model that uses cropped maxillary anterior teeth to determine the presence of mesiodens. The data were divided into five groups and cross-validated. Deep learning model were created and trained using Inception-ResNet-v2. The performance of the segmentation network was evaluated using accuracy, Intersection over Union (IoU), and MeanBFscore. The overall network performance was evaluated using accuracy, precision, recall, and F1-score. RESULTS: Segmentation performance using posterior molar space in panoramic radiographs was 0.839, IoU 0.762, and MeanBFscore 0.907. The mean values of accuracy, precision, recall, F1-score, and area under the curve for the diagnosis of mesiodens using automatic segmentation were 0.971, 0.971, 0.971, 0.971, and 0.971, respectively. CONCLUSIONS: The diagnostic performance of the deep learning system using posterior molar space on the panoramic radiograph was sufficiently useful. The results of the deep learning system confirmed the possibility of complete automation of the classification of mesiodens.

Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.

Tomographic similarity scan with a computed modified absolute mandibular midsagittal plane for precise and objective localization of mandibular asymmetry.

The application of 3D imaging is at its cusp in craniofacial diagnosis and treatment planning. However, most applications are limited to simple subjective superimposition-based analysis. As the diagnostic accuracy dictates the precision in operability, we propose a novel method that enables objective clinical decision making for patients with mandibular asymmetry. We analyzed cone-beam computed tomography (CBCT) scans of 34 patients who underwent surgical correction for mandibular asymmetry using a high-throughput computing algorithm. Radiomic segmentation of quantitative features of surface and volume followed by exploration resulted in identification of a computed modified absolute mandibular midsagittal plane (cmAMP). Tomographic similarity scan (ToSS) curves were generated via bilateral equidistant scanning in an antero-posterior direction with cmAMP as the reference. ToSS comprised of a comprehensive similarity index (SI) score curve and a segment-wise volume curve. The SI score was computed using the Sørensen-Dice similarity coefficient ranging from 0 to 1. The volumetric analysis was represented as the non-overlapping volume (NOV) and overlapping volume (OV) for each segment, with two segmentation lines, at the mental foramen anteriorly and the intraoral vertical ramus osteotomy region posteriorly. Statistical analysis showed strong negative correlation between the NOV and SI scores for the anterior, middle, and total mandible (P < 0.001). Additionally, a significant correlation was observed between the change in the SI scores for anterior (P = 0.044) and middle segments (P < 0.001) to the total mandible when comparing the data before and after the surgery. This work demonstrated the potential of incorporating ToSS curves in surgical simulation software to improve precision in the clinical decision-making process.

Comparison of panoramic radiography and cone-beam computed tomography for assessing radiographic signs indicating root protrusion into the maxillary sinus.

PURPOSE: This study investigated correlations between findings on panoramic radiographs and cone-beam computed tomography (CBCT) to assess the relationship between the maxillary sinus floor and the roots of maxillary posterior teeth. In addition, radiographic signs indicating actual root protrusion into the maxillary sinus were evaluated on panoramic radiographs. MATERIALS AND METHODS: Paired panoramic radiographs and CBCT images from 305 subjects were analyzed. This analysis classified 2,440 maxillary premolars and molars according to their relationship with the maxillary sinus floor on panoramic radiographs and CBCT images. In addition, interruption of the sinus floor was examined on panoramic radiographs. RESULTS: Root protrusion into the maxillary sinus occurred most frequently in the mesiobuccal roots of the second molars. The classification according to panoramic radiographs and CBCT images was the same in more than 90% of cases when there was no contact between the root apex and the sinus floor. When the panoramic radiograph showed root protrusion into the sinus, the CBCT images showed the same classification in 67.5% of second molars, 48.8% of first molars, and 53.3% of second premolars. There was a statistically significant relationship between interruption of the sinus floor on panoramic radiographs and root protrusion into the sinus on CBCT images. CONCLUSION: The presence of root protrusion into the sinus on panoramic radiographs demonstrated a moderate ability to predict root protrusion into the maxillary sinus. Interruption of the maxillary sinus floor could be considered an indicator of actual root protrusion into the maxillary sinus.

Ameloblastic carcinoma of the mandible: A case report.

Ameloblastic carcinoma is a rare odontogenic malignant tumor with the histologic features of both ameloblastoma and carcinoma. It occurs more frequently in the mandible than in the maxilla and it may appear de novo or develop from a preexisting ameloblastoma or odontogenic cyst. Rapidly progressing, painful swelling is the most common symptom, and radiographically, it shows significant bone resorption and cortical perforation. This report described a case of ameloblastic carcinoma in a 45-year-old man who presented with swelling in the left mandible. The lesion showed combined features of benign findings, such as an expansile cortex with a distinct border, and malignant findings, such as a large exophytic mass with frank bone resorption. Excisional biopsy was performed and a final diagnosis of ameloblastic carcinoma was made.

Very deep super-resolution for efficient cone-beam computed tomographic image restoration.

PURPOSE: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. MATERIALS AND METHODS: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. RESULTS: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. CONCLUSION: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

Dose assessment in dental cone-beam computed tomography: Comparison of optically stimulated luminescence dosimetry with Monte Carlo method.

The variety of cone-beam computed tomography (CBCT) machines and their applications has rapidly increased in recent years, making the dose evaluation of individual devices an important issue. Patient doses from CBCT were assessed with two different methods: optically stimulated luminescence dosimeter (OSLD) measurements and Monte Carlo (MC) simulations, in four different examination modes. Based on an analysis of the measurement process and the obtained values, a recommendation is made regarding which method is more practical and efficient for acquiring the effective dose of CBCT. Twenty-two OSLDs were calibrated and equipped in human phantoms of head and neck organs. They were exposed to radiation from two CBCT units-CS9300 (Carestream Dental LLC, Atlanta, Georgia) and RAYSCAN α+ (Ray Co. Ltd, Hwaseong-si, Korea)-using two different examination modes. The dose recorded using the OSLDs was used to calculate the organ dose and the effective dose for each unit in each examination mode. These values were also calculated using MC software, PCXMC (STUK, Helsinki, Finland). The organ doses and effective doses obtained using both methods were compared for each examination mode of the individual units. The OSLD-measured effective dose value was higher than that obtained using the MC method for each examination mode, except the dual jaw mode of CS9300. The percent difference of the effective dose between the two methods ranged from 4.0% to 14.3%. The dose difference between the methods decreased as the field of view became smaller. The organ dose values varied according to the method, although the overall trend was similar for both methods. The organs showing high doses were mostly consistent for both methods. In this study, the effective dose obtained by OSLD measurements and MC simulations were compared, and both methods were described in detail. As a relatively efficient and easy-to-perform method, we cautiously suggest using MC simulations for dose evaluations in the future.

Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs.

In the absence of accurate medical records, it is critical to correctly classify implant fixture systems using periapical radiographs to provide accurate diagnoses and treatments to patients or to respond to complications. The purpose of this study was to evaluate whether deep neural networks can identify four different types of implants on intraoral radiographs. In this study, images of 801 patients who underwent periapical radiographs between 2005 and 2019 at Yonsei University Dental Hospital were used. Images containing the following four types of implants were selected: Brånemark Mk TiUnite, Dentium Implantium, Straumann Bone Level, and Straumann Tissue Level. SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50 were tested to determine the optimal pre-trained network architecture. The accuracy, precision, recall, and F1 score were calculated for each network using a confusion matrix. All five models showed a test accuracy exceeding 90%. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed an accuracy of approximately 96% and 97%, respectively. The results of this study confirmed that convolutional neural networks can classify the four implant fixtures with high accuracy even with a relatively small network and a small number of images. This may solve the inconveniences associated with unnecessary treatments and medical expenses caused by lack of knowledge about the exact type of implant.

Gastrin-Releasing Peptide (GRP) Stimulates Osteoclastogenesis in Periodontitis.

Periodontitis is a chronic inflammatory disease with alveolar bone resorption and subsequent tooth loss as its ultimate outcomes. Gastrin-releasing peptide (GRP) is a neuropeptide with growth-stimulatory and tumorigenic properties, and neuropeptides have previously been suggested to play a role in the complex cascade of chemical activity associated with periodontal inflammation. In this study, GRP treatment enhanced the differentiation of bone marrow-derived macrophages (BMMs) into osteoclasts, and gastrin-releasing peptide receptor (GRPR) antagonists suppressed the pro-osteoclastogenic effect of GRP. Grpr-siRNA knockdown resulted in a significantly lower number of osteoclasts formed as compared with the control. Interestingly, gene expression analysis indicated downregulation of Grp and Grpr expressions in BMMs during osteoclastogenesis. Moreover, ligature-induced periodontitis model in mice and gingival samples from patients with periodontitis displayed increased immunostaining of GRP in the oral epithelium. Subsequently, stimulation of mouse primary epithelial cells (ECs) and HaCaT cells, human epidermal keratinocytes, with lipopolysaccharides (LPS) of Porphyromonas gingivalis or live P. gingivalis upregulated Grp and Grpr expressions. Finally, coculture of P. gingivalis-stimulated ECs and BMMs using Transwell system revealed that the differentiation of BMMs was induced when subjected to paracrine activation by LPS- as well as live-P. gingivalis stimulated ECs. Taken together, our results demonstrate that the pro-osteoclastogenic properties of BMMs may be modulated by GRP produced by ECs in the periodontal microenvironment.

Automatic mandibular canal detection using a deep convolutional neural network.

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients' discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.

Non-invasive ventilation for acute respiratory failure: pressure support ventilation vs. pressure-controlled ventilation.

BACKGROUND: The best ventilator mode for patients receiving non-invasive ventilation (NIV) has not been clarified. This study compared the effectiveness of two pressure-targeted modes, i.e., pressure support ventilation (PSV) and pressure-controlled ventilation (PCV), in patients receiving NIV. METHODS: This was a prospective multicentre observational study of NIV use for acute respiratory failure (ARF) in adult patients. We compared the two pressure-targeted modes in terms of NIV success and complication rates. RESULTS: Among 176 patients receiving NIV, 88 patients were included in the study (PCV mode, n=29; PSV mode, n=59). The study population had a median age of 73.0 years and median body mass index of 20.8 kg/m2. The applied inspiratory positive airway pressure (IPAP) was higher in patients with PCV than in those with PSV [18.0 cmH2O (15.0-20.5 cmH2O) vs. 15.0 cmH2O (12.0-17.0 cmH2O), respectively, P=0.001]. More patients with PCV received sedatives and experienced dry mouth than those with PSV; however, the incidences of large leaks were low in both groups (n=5 vs. n=2, respectively). With regard to NIV outcomes, 24 (27.2%) patients experienced NIV failure and 13 (14.8%) died in hospital. PSV mode was a significant factor for NIV success [odds ratio (OR), 2.303; 95% confidence interval (CI), 1.216 to 4.360] in multivariate analyses and this association remained significant in a 1:1 matched cohort (n=29 per group). CONCLUSIONS: In contrast to PCV mode, PSV mode was significantly associated with NIV success in the intensive care unit setting, particularly when large leaks were not a major concern. Nevertheless, further well-designed multicenter, protocol-driven randomized controlled trials are warranted.