Evaluation of Aligners and Root Resorption: An Overview of Systematic Reviews.

Background: To evaluate the current evidence on clear aligners and root resorption using 3D and/or combined 2D and 3D methods from available systematic reviews and meta-analyses and to determine the relationship between root resorption and clear aligners using the AMSTAR 2 tool. Methods: A comprehensive literature search of systematic reviews investigating aligners and root resorption, published up until 31 December 2022, was conducted. The following electronic databases were searched: MEDLINE via PubMed, EMBASE, Google Scholar, Science Direct, Web of Science, Scopus, LIVIVO, and LILACS. There were no language restrictions. The inclusion criteria were restricted to studies focusing on root resorption utilizing either 3D methods exclusively or a combination of 2D and 3D techniques. Data were screened and analyzed for quality using the "A Measurement Tool to Assess Systematic Reviews (AMSTAR 2)" tool. Data extraction was conducted independently by two authors. The gathered information was categorized and synthesized narratively based on the primary findings elucidated within the reviews. Results: Out of a total of 1221 potentially eligible studies initially identified, 4 systematic reviews met the inclusion criteria following the exclusion of irrelevant studies. Among these, two systematic reviews (50%) were classified as low-quality, while the remaining two (50%) were deemed to be of critically low quality. Conclusions: Based on the findings of four systematic reviews, the root resorption rate was lower with the use of clear aligners than with fixed aligners. It is advisable to approach the interpretation of this conclusion with caution, as the quality of the available evidence is assessed to be very low. Higher quality systematic reviews are needed to substantiate this conclusion.

Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study.

Objective: : To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: : A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: : The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: : The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets.

BACKGROUND AND OBJECTIVE: Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. METHODS: 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. RESULTS: In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). CONCLUSION: We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.

The prediction of sagittal chin point relapse following two-jaw surgery using machine learning.

The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patients (110 males and 117 females, 207 training and 20 test sets). Using lateral cephalograms taken at the initial evaluation (T0), pretreatment (T1), after (T2) 2 J-OGS, and post treatment (T3), 55 linear and angular skeletal and dental surgical movements (T2-T1) were measured. Six ML modes were utilized, including classification and regression trees (CART), conditional inference tree (CTREE), and random forest (RF). The training samples were classified into three groups; highly significant (HS) (≥ 4), significant (S) (≥ 2 and < 4), and insignificant (N), depending on Pog relapse. RF indicated that the most important variable that affected relapse rank prediction was ramus inclination (RI), CTREE and CART revealed that a clockwise rotation of more than 3.7 and 1.8 degrees of RI was a risk factor for HS and S groups, respectively. RF, CTREE, and CART were practical tools for predicting surgical stability. More than 1.8 degrees of CW rotation of the ramus during surgery would lead to significant Pog relapse.

Three-dimensional evaluation of the association between tongue position and upper airway morphology in adults: A cross-sectional study.

Objective: This study aimed to evaluate the association between low tongue position (LTP) and the volume and dimensions of the nasopharyngeal, retropalatal, retroglossal, and hypopharyngeal segments of the upper airway. Methods: A total of 194 subjects, including 91 males and 103 females were divided into a resting tongue position (RTP) group and a LTP group according to their tongue position. Subjects in the LTP group were divided into four subgroups (Q1, Q2, Q3, and Q4) according to the intraoral space volume. The 3D slicer software was used to measure the volume and minimum and average cross-sectional areas of each group. Airway differences between the RTP and LTP groups were analyzed to explore the association between tongue position and the upper airway. Results: No significant differences were found in the airway dimensions between the RTP and LTP groups. For both retropalatal and retroglossal segments, the volume and average cross-sectional area were significantly greater in the patients with extremely low tongue position. Regression analysis showed that the retroglossal airway dimensions were positively correlated with the intraoral space volume and negatively correlated with A point-nasion-B point and palatal plane to mandibular plane. Males generally had larger retroglossal and hypopharyngeal airways than females. Conclusions: Tongue position did not significantly influence upper airway volume or dimensions, except in the extremely LTP subgroup.

Survival rates of mandibular fixed retainers: comparison of a tube-type retainer and conventional multistrand retainers : A prospective randomized clinical trial.

OBJECTIVE: The purposes of this study were to evaluate the survival rate of a tube-type mandibular fixed retainer and compare it to conventional multistrand retainers. MATERIALS AND METHODS: In all, 66 patients who had completed their orthodontic treatment were enrolled in this study. They were allocated randomly to a tube-type retainer group or a 0.020 multistrand fixed retainer group. In case of the tube-type retainer, a thermoactive 0.012 NiTi was placed into 6 mini-tubes passively bonded to the anterior teeth. The patients were recalled at 1, 3, 6, 12, and 24 months after retainer placement. During the 2­year follow-up period, any first-time failure of retainers was recorded. Kaplan-Meier survival analysis and log-rank tests were used to compare the failure rates between the two types of retainers. RESULTS: Of the 34 patients, 14 (41.2%) showed failure in the multistrand retainer group, whereas only 2 of 32 (6.3%) reported failure in the tube-type retainer group. There was a statistically significant difference in failure between the multistrand retainer and the tube-type retainer (log-rank test, P = 0.001). The hazard ratio was 11.937 (95% confidence interval 2.708-52.620; P = 0.005). CONCLUSION: The tube-type retainer can be used with fewer concerns about recurrent retainer detachments during orthodontic retention.

Long-term follow-up implant site development in the submerged mandibular primary second molars: a case report.

Treatment of ankylosed and submerged primary molars without permanent successors is challenging, as normal vertical dentoalveolar growth is compromised. Thus, grafting techniques and distraction osteogenesis are performed for ridge augmentation before implant restoration. However, these techniques are invasive with limited success. Another treatment for implant site development is noninvasive forced eruption. This case report describes long-term follow-up of alveolar ridge augmentation in the submerged mandibular primary second molars using subluxation and orthodontic forced eruption for implant site development. A 19-year old female had Class II molar relationships, upper anterior crowding with large overjet, missing four second premolars and submerged mandibular primary second molars with inadequate vertical development of alveolar bone. For the vertical alveolar bone alterations in the mandible, forced eruption with subluxation of ankylosed lower primary second molars was applied. Treatment outcome was evaluated over 5 years with stable occlusion, healthy periodontal tissues, and successful radiographic results.

Comparison of short-term condylar positional changes in mandibular prognathism after surgery-first approach: Symmetric setback versus asymmetric setback.

The aim of this study was to compare how the displacement of the mandibular condyle changed after symmetric or asymmetric mandibular setback surgery using the surgery-first approach (SFA). Patients who underwent mandibular setback surgery using the SFA were selected and divided into a symmetry group (n = 18) with differences of less than 2 mm between the right and left setback, and an asymmetry group (n = 18) with a difference of greater than 2 mm. Cone-beam computed tomography (CBCT)-generated cephalograms were obtained after three-dimensional superimposition of CBCT images taken before surgery (T0), within one week after surgery (T1), and seven months after surgery (T2). The condylar positions were measured. Condylar positional changes according to time were compared between the two groups and correlation analysis was performed. There were significant positional changes in mandibular condyles over time in both groups. However, most of these changes returned to their initial state. In the asymmetry group, there was a greater internal rotation of the mandibular condyle on the lesser setback side. The correlation analysis results revealed that only the setback difference was associated with rotational displacement of the condyle on the lesser setback side at two time points (T1-T0, T2-T0). In the SFA, significant condylar displacement occurred immediately after both symmetric and asymmetric mandibular setback surgery, and the right/left difference in mandibular setback showed a significant positive correlation with rotational displacement. Although more significant rotational displacement of the mandibular condyle was observed after asymmetric mandibular setback surgery, the amount was not large enough to be clinically significant.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery.

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Sclerostin in periodontal ligament: Homeostatic regulator in biophysical force-induced tooth movement.

AIM: To study the role of sclerostin in periodontal ligament (PDL) as a homeostatic regulator in biophysical-force-induced tooth movement (BFTM). MATERIALS AND METHODS: BFTM was performed in rats, followed by microarray, immunofluorescence, in situ hybridization, and real-time polymerase chain reaction for the detection and identification of the molecules. The periodontal space was analysed via micro-computed tomography. Effects on osteoclastogenesis and bone resorption were evaluated in the bone-marrow-derived cells in mice. In vitro human PDL cells were subjected to biophysical forces. RESULTS: In the absence of BFTM, sclerostin was hardly detected in the periodontium except in the PDL and alveolar bone in the furcation region and apex of the molar roots. However, sclerostin was up-regulated in the PDL in vivo by adaptable force, which induced typical transfiguration without changes in periodontal space as well as in vitro PDL cells under compression and tension. In contrast, the sclerostin level was unaffected by heavy force, which caused severe degeneration of the PDL and narrowed periodontal space. Sclerostin inhibited osteoclastogenesis and bone resorption, which corroborates the accelerated tooth movement by the heavy force. CONCLUSIONS: Sclerostin in PDL may be a key homeostatic molecule in the periodontium and a biological target for the therapeutic modulation of BFTM.

Vertical bony step between proximal and distal segments after mandibular setback is related with relapse: A cone-beam computed tomographic study.

INTRODUCTION: Vertical bony step (VBS) occurs between proximal and distal segments of the mandible during mandibular setback surgery with bilateral sagittal split ramus osteotomy. The purpose of this study was to investigate whether VBS is correlated with the relapse of mandibular setback using 3-dimensional models constructed from cone-beam computed tomography. METHODS: The subjects consisted of 30 patients who underwent bilateral sagittal split ramus osteotomy for a mandibular setback. Double jaw surgery was performed in 18 patients, and isolated mandibular setback surgery was performed in 12 patients. Cone-beam computed tomography scans were taken at pretreatment (T0), postsurgery (T1), and posttreatment (T2). Treatment changes and the correlations between measurements were evaluated. RESULTS: The mean mandibular setback was -11.9 mm, and the mean VBS was -5.6 mm. Correlations with the relapse of mandibular setback were found in the amount of mandibular setback (T1 - T0), development of VBS (T1 - T0), posterior movement of the proximal segment (T1 - T0), counterclockwise rotation of symphysis (T2 - T1), and the resolution of VBS (T2 - T1). CONCLUSIONS: The development and resolution of VBS were correlated with the relapse of mandibular setback. Minimizing VBS is recommended to reduce the relapse of mandibular setback.

Periodontal ligament preloading and rapid prototyping of the donor tooth in the autotransplantation of premolars with complete root formation.

This case report describes the successful orthodontic treatment of an 11-year-old girl with skeletal Class II malocclusion and congenitally missing mandibular second premolars. To resolve her upper lip protrusion and restore the missing mandibular premolars, extraction of the maxillary first premolars and subsequent autotransplantation of the extracted premolars onto the site of the missing mandibular second premolars were performed. To ensure the success of the autotransplantation and subsequent orthodontic treatment, an orthodontic force was preapplied on the donor teeth, and the recipient sockets were prepared with the aid of replica teeth. Thereafter, comprehensive orthodontic treatment was performed to close the extraction space in the maxilla and align the mandibular dentition, including the transplants. The patient achieved a functional occlusion with an improved facial profile. Results of the orthodontic treatment and autotransplantation were stable during the 5-year follow-up. On the basis of this report, a management protocol for a biomechanically enhanced autotransplantation procedure was suggested. This approach would enable an effective treatment procedure, thereby increasing the usefulness of autotransplantation.

Association of the three-dimensional skeletal variables with self-recognition of facial asymmetry in skeletal Class III patients.

OBJECTIVES: To investigate the association between three-dimensional (3D) skeletal variables and self-recognition of facial asymmetry in skeletal Class III patients. MATERIALS AND METHODS: Questionnaires and cone beam computed tomography of 74 patients (42 men and 32 women; mean age: 22.8 ± 4.5 years) with skeletal Class III and facial asymmetry were collected retrospectively. Patients were classified into three groups: group Sy (recognition of symmetry), group NS (not sure), and group Asy (recognition of asymmetry), according to their responses to the questionnaires. To assess 3D anatomic differences in the maxillomandibular region, six 3D hard tissue variables: maxillary height, ramal length, frontal ramal inclination (FRI), lateral ramal inclination (LRI), mandibular body length (Mn BL), and mandibular body height (Mn BH) were compared among the three self-recognition groups. Six 3D hard tissue variables and Menton deviation were reduced into three factors and their association with the self-recognition of facial asymmetry was investigated. RESULTS: Maxillary height, FRI, LRI, Mn BH, and Menton deviation demonstrated significant differences among the three self-recognition groups. The reduced factors, which consisted of transverse and vertical parameters, and vertical parameter of the mandibular corpus, demonstrated significant differences among the three self-recognition groups. The difference in Mn BH influenced the self-recognition of facial asymmetry. CONCLUSIONS: Both the transverse and vertical parameter of the skeleton were determinant in self-recognition of facial asymmetry. Identification of the skeletal difference in the lateral view involving LRI and Mn BH should be included for assessment of facial asymmetry.

Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals.

OBJECTIVE: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. METHODS: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). RESULTS: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. CONCLUSIONS: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

Comparison of one-jaw and two-jaw orthognathic surgery in patients with skeletal Class III malocclusion using data from 10 multi-centers in Korea: Part I. Demographic and skeletodental characteristics.

OBJECTIVE: To investigate demographic and skeletodental characteristics of one-jaw (1J-OGS) and two-jaw orthognathic surgery (2J-OGS) in patients with skeletal Class III malocclusion. METHODS: 750 skeletal Class III patients who underwent OGS at 10 university hospitals in Korea between 2015 and 2019 were investigated; after dividing them into the 1J-OGS (n = 186) and 2J-OGS groups (n = 564), demographic and skeletodental characteristics were statistically analyzed. RESULTS: 2J-OGS was more frequently performed than 1J-OGS (75.2 vs. 24.8%), despite regional differences (capital area vs. provinces, 86.6 vs. 30.7%, p < 0.001). Males outnumbered females, and their mean operation age was older in both groups. Regarding dental patterns, the most frequent maxillary arch length discrepancy (ALD) was crowding in the 1J-OGS group (52.7%, p < 0.001) and spacing in the 2J-OGS group (40.4%, p < 0.001). However, the distribution of skeletal pattern was not significantly different between the two groups (all p > 0.05). The most prevalent skeletal patterns in both groups were hyper-divergent pattern (50.0 and 54.4%, respectively) and left-side chin point deviation (both 49.5%). Maxillary spacing (odds ratio [OR], 3.645; p < 0.001) increased the probability of 2J-OGS, while maxillary crowding (OR, 0.672; p < 0.05) and normo-divergent pattern (OR, 0.615; p < 0.05) decreased the probability of 2J-OGS. CONCLUSIONS: In both groups, males outnumbered females, and their mean operation age was older. The most frequent ALD was crowding in the 1J-OGS group, and spacing in the 2J-OGS group, while skeletal characteristics were not significantly different between the two groups.

Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers.

INTRODUCTION: The purpose of this study was to evaluate the accuracy of auto-identification of the posteroanterior (PA) cephalometric landmarks using the cascade convolution neural network (CNN) algorithm and PA cephalogram images of a different quality from nationwide multiple centers nationwide. METHODS: Of the 2798 PA cephalograms from 9 university hospitals, 2418 images (2075 training set and 343 validation set) were used to train the CNN algorithm for auto-identification of 16 PA cephalometric landmarks. Subsequently, 99 pretreatment images from the remaining 380 test set images were used to evaluate the accuracy of auto-identification of the CNN algorithm by comparing with the identification by a human examiner (gold standard) using V-Ceph 8.0 (Ostem, Seoul, South Korea). Pretreatment images were used to eliminate the effects of orthodontic bracket, tube and wire, surgical plate, and surgical screws. Paired t test was performed to compare the x- and y-coordinates of each landmark. The point-to-point error and the successful detection rate (range, within 2.0 mm) were calculated. RESULTS: The number of landmarks without a significant difference between the location identified by the human examiner and by auto-identification by the CNN algorithm were 8 on the x-coordinate and 5 on the y-coordinate, respectively. The mean point-to-point error was 1.52 mm. The low point-to-point error (<1.0 mm) was observed at the left and right antegonion (0.96 mm and 0.99 mm, respectively) and the high point-to-point error (>2.0 mm) was observed at the maxillary right first molar root apex (2.18 mm). The mean successful detection rate of auto-identification was 83.3%. CONCLUSIONS: Cascade CNN algorithm for auto-identification of PA cephalometric landmarks showed a possibility of an effective alternative to manual identification.

Infrared Clinical Enamel Crack Detector Based on Silicon CCD and Its Application: A High-Quality and Low-Cost Option.

Enamel cracks generated in the anterior teeth not only affect the function but also the aesthetics of the teeth. Chair-side tooth enamel crack detection is essential for clinicians to formulate treatment plans and prevent related dental disease. This study aimed to develop a dental imaging system using a near-IR light source to detect enamel cracks and to investigate the relationship between anterior enamel cracks and age in vivo. A total of 68 subjects were divided into three groups according to their age: young, middle, and elderly. Near-infrared radiation of 850 nm was used to identify enamel cracks in anterior teeth. The results of the quantitative examination showed that the number of enamel cracks on the teeth increased considerably with age. For the qualitative examination, the results indicated that there was no significant relationship between the severity of the enamel cracks and age. So, it can be concluded that the prevalence of anterior cracked tooth increased significantly with age in the young and middle age. The length of the anterior enamel cracks tended to increase with age too; however, this result was not significant. The silicon charge-coupled device (CCD) with a wavelength of 850 nm has a good performance in the detection of enamel cracks and has very good clinical practicability.

The Potential Role of Human NME1 in Neuronal Differentiation of Porcine Mesenchymal Stem Cells: Application of NB-hNME1 as a Human NME1 Suppressor.

This study aimed to investigate the effects of the human macrophage (MP) secretome in cellular xenograft rejection. The role of human nucleoside diphosphate kinase A (hNME1), from the secretome of MPs involved in the neuronal differentiation of miniature pig adipose tissue-derived mesenchymal stem cells (mp AD-MSCs), was evaluated by proteomic analysis. Herein, we first demonstrate that hNME1 strongly binds to porcine ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 1 (pST8SIA1), which is a ganglioside GD3 synthase. When hNME1 binds with pST8SIA1, it induces degradation of pST8SIA1 in mp AD-MSCs, thereby inhibiting the expression of ganglioside GD3 followed by decreased neuronal differentiation of mp AD-MSCs. Therefore, we produced nanobodies (NBs) named NB-hNME1 that bind to hNME1 specifically, and the inhibitory effect of NB-hNME1 was evaluated for blocking the binding between hNME1 and pST8SIA1. Consequently, NB-hNME1 effectively blocked the binding of hNME1 to pST8SIA1, thereby recovering the expression of ganglioside GD3 and neuronal differentiation of mp AD-MSCs. Our findings suggest that mp AD-MSCs could be a potential candidate for use as an additive, such as an immunosuppressant, in stem cell transplantation.

Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres.

OBJECTIVE: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. SETTINGS AND SAMPLE POPULATION: A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. MATERIALS AND METHODS: We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. RESULTS: The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01). CONCLUSION: The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.