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1.
Artigo em Inglês | MEDLINE | ID: mdl-33919492

RESUMO

Dental implants are among the most common treatments for missing teeth. The thickness of the crestal cortical bone at the potential dental implant site is a critical factor affecting the success rate of dental implant surgery. However, previous studies have predominantly focused on female patients, who are at a high risk of osteoporosis, for the discussion of bone quality and quantity at the dental implant site. This study aimed to investigate the effect of male patients' age on the crestal cortical bone of the jaw at the dental implant site by using dental cone-beam computed tomography (CBCT). This study performed dental CBCT on 84 male patients of various ages to obtain tomograms of 288 dental implant sites at the jawbone (41 sites in the anterior maxilla, 95 in the posterior maxilla, 59 in the anterior mandible, and 93 in the posterior mandible) for measuring the cortical bone thickness. A one-way analysis of variance and Scheffe's test were performed on the measurement results to compare the cortical bone thickness at implant sites in the four jaw areas. The correlation between male patient age and cortical bone thickness at the dental implant site was determined. The four jaw areas in order of the cortical bone thickness were as follows: posterior mandible (1.07 ± 0.44 mm), anterior mandible (0.99 ± 0.30 mm), anterior maxilla (0.82 ± 0.32 mm), and posterior maxilla (0.71 ± 0.27 mm). Apart from dental implant sites in the anterior and posterior mandibles, no significant correlation was observed between male patients' age and the cortical bone thickness at the dental implant site.

2.
IEEE Trans Biomed Eng ; PP2021 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-33819146

RESUMO

OBJECTIVE: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer's Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. METHODS: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared L2-norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. RESULTS: We have conducted extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. CONCLUSION: All these promising experimental results have demonstrated the effectiveness of our new method. SIGNIFICANCE: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online.

3.
Artigo em Inglês | MEDLINE | ID: mdl-33667166

RESUMO

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic QN (SQN) methods have been developed to achieve a better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic QN method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of O(n+n1/2ε⁻²) in the finite-sum setting to obtain an ε-first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of O(ε⁻³) is developed, which also matches the existing best result. Extensive experiments on benchmark data sets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization.

4.
World J Gastroenterol ; 27(8): 737-750, 2021 Feb 28.
Artigo em Inglês | MEDLINE | ID: mdl-33716451

RESUMO

BACKGROUND: Lymph node metastasis (LNM) affects the application and outcomes of endoscopic resection in T1 esophageal squamous cell carcinoma (ESCC). However, reports of the risk factors for LNM have been controversial. AIM: To evaluate risk factors for LNM in T1 ESCC. METHODS: We searched Embase, PubMed and Cochrane Library to select studies related to LNM in patients with T1 ESCC. Included studies were divided into LNM and non-LNM groups. We performed a meta-analysis to examine the relationship between LNM and clinicopathologic features. Odds ratio (OR), mean differences and 95% confidence interval (CI) were assessed using a fixed-effects or random-effects model. RESULTS: Seventeen studies involving a total of 3775 patients with T1 ESCC met the inclusion criteria. After excluding studies with heterogeneity based on influence analysis, tumor size (OR = 1.93, 95%CI = 1.49-2.50, P < 0.001), tumor location (OR = 1.46, 95%CI = 1.17-1.82, P < 0.001), macroscopic type (OR = 3.17, 95%CI = 2.33-4.31, P < 0.001), T1 substage (OR = 6.28, 95%CI = 4.93-8.00, P < 0.001), differentiation (OR = 2.11, 95%CI = 1.64-2.72, P < 0.001) and lymphovascular invasion (OR = 5.86, 95%CI = 4.60-7.48, P < 0.001) were found to be significantly associated with LNM. Conversely, sex, age and infiltrative growth pattern were not identified as risk factors for LNM. CONCLUSION: A tumor size > 2 cm, lower location, nonflat macroscopic type, T1b stage, poor differentiation and lymphovascular invasion were associated with LNM in patients with T1 ESCC.

5.
Clin Oral Investig ; 2021 Mar 04.
Artigo em Inglês | MEDLINE | ID: mdl-33665683

RESUMO

OBJECTIVE: The study objective was to investigate four common occlusal modes by using the finite element (FE) method and to conduct a biomechanical analysis of the periodontal ligament (PDL) and surrounding bone when orthodontic force is applied. MATERIALS AND METHODS: A complete mandibular FE model including teeth and the PDL was established on the basis of cone-beam computed tomography images of an artificial mandible. In the FE model, the left and right mandibular first premolars were not modeled because both canines required distal movement. In addition, four occlusal modes were simulated: incisal clench (INC), intercuspal position (ICP), right unilateral molar clench (RMOL), and right group function (RGF). The effects of these four occlusal modes on the von Mises stress and strain of the canine PDLs and bone were analyzed. RESULTS: Occlusal mode strongly influenced the distribution and value of von Mises strain in the canine PDLs. The maximum von Mises strain values on the canine PDLs were 0.396, 1.811, 0.398, and 1.121 for INC, ICP, RMOL, and RGF, respectively. The four occlusal modes had smaller effects on strain distribution in the cortical bone, cancellous bone, and miniscrews. CONCLUSION: Occlusal mode strongly influenced von Mises strain on the canine PDLs when orthodontic force was applied. CLINICAL RELEVANCE: When an FE model is used to analyze the biomechanical behavior of orthodontic treatments, the effect of muscle forces caused by occlusion must be considered.

6.
BMC Musculoskelet Disord ; 22(1): 146, 2021 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-33546670

RESUMO

BACKGROUND: Metacarpal shaft fracture is a common fracture in hand trauma injuries. Surgical intervention is indicated when fractures are unstable or involve considerable displacement. Current fixation options include Kirschner wire, bone plates, and intramedullary headless screws. Common complications include joint stiffness, tendon irritation, implant loosening, and cartilage damage. OBJECTIVE: We propose a modified fixation approach using headless compression screws to treat transverse or short-oblique metacarpal shaft fracture. MATERIALS AND METHODS: We used a saw blade to model transverse metacarpal neck fractures in 28 fresh porcine metacarpals, which were then treated with the following four fixation methods: (1) locked plate with five locked bicortical screws (LP group), (2) regular plate with five bicortical screws (RP group), (3) two Kirschner wires (K group), and (4) a headless compression screw (HC group). In the HC group, we proposed a novel fixation model in which the screw trajectory was oblique to the long axis of the metacarpal bone. The entry point of the screw was in the dorsum of the metacarpal neck, and the exit point was in the volar cortex of the supracondylar region; thus, the screw did not damage the articular cartilage. The specimens were tested using a modified three-point bending test on a material testing system. The maximum fracture forces and stiffness values of the four fixation types were determined by observing the force-displacement curves. Finally, the Kruskal-Wallis test was adopted to process the data, and the exact Wilcoxon rank sum test with Bonferroni adjustment was performed to conduct paired comparisons among the groups. RESULTS: The maximum fracture forces (median ± interquartile range [IQR]) of the LP, RP, HC, and K groups were 173.0 ± 81.0, 156.0 ± 117.9, 60.4 ± 21.0, and 51.8 ± 60.7 N, respectively. In addition, the stiffness values (median ± IQR) of the LP, HC, RP, and K groups were 29.6 ± 3.0, 23.1 ± 5.2, 22.6 ± 2.8, and 14.7 ± 5.6 N/mm, respectively. CONCLUSION: Headless compression screw fixation provides fixation strength similar to locked and regular plates for the fixation of metacarpal shaft fractures. The headless screw was inserted obliquely to the long axis of the metacarpal bone. The entry point of the screw was in the dorsum of the metacarpal neck, and the exit point was in the volar cortex of the supracondylar region; therefore the articular cartilage iatrogenic injury can be avoidable. This modified fixation method may prevent tendon irritation and joint cartilage violation caused by plating and intramedullary headless screw fixation.

7.
Artigo em Inglês | MEDLINE | ID: mdl-33591910

RESUMO

Kernel methods have achieved tremendous success in the past two decades. In the current big data era, data collection has grown tremendously. However, existing kernel methods are not scalable enough both at the training and predicting steps. To address this challenge, in this paper, we first introduce a general sparse kernel learning formulation based on the random feature approximation, where the loss functions are possibly non-convex. In order to reduce the scale of random features required in experiment, we also use that formulation based on the orthogonal random feature approximation. Then we propose a new asynchronous parallel doubly stochastic algorithm for large scale sparse kernel learning (AsyDSSKL). To the best our knowledge, AsyDSSKL is the first algorithm with the techniques of asynchronous parallel computation and doubly stochastic optimization. We also provide a comprehensive convergence guarantee to AsyDSSKL. Importantly, the experimental results on various large-scale real-world datasets show that, our AsyDSSKL method has the significant superiority on the computational efficiency at the training and predicting steps over the existing kernel methods.

8.
IEEE Trans Image Process ; 30: 2045-2059, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33449878

RESUMO

Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this task still remains challenging. Recently, deep learning based methods have been widely employed to solve these problems and can be categorized into proposal-free and proposal-based methods. However, both proposal-free and proposal-based methods suffer from information loss, as they focus on either global-level semantic or local-level instance features. To tackle this issue, we present a Panoptic Feature Fusion Net (PFFNet) that unifies the semantic and instance features in this work. Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch. Then, a mask quality sub-branch is designed to align the confidence score of each object with the quality of the mask prediction. Furthermore, a consistency regularization mechanism is designed between the semantic segmentation tasks in the semantic and instance branches, for the robust learning of both tasks. Extensive experiments demonstrate the effectiveness of our proposed PFFNet, which outperforms several state-of-the-art methods on various biomedical and biological datasets.

9.
JAMA ; 2020 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-33300950

RESUMO

Importance: Among all subtypes of breast cancer, triple-negative breast cancer has a relatively high relapse rate and poor outcome after standard treatment. Effective strategies to reduce the risk of relapse and death are needed. Objective: To evaluate the efficacy and adverse effects of low-dose capecitabine maintenance after standard adjuvant chemotherapy in early-stage triple-negative breast cancer. Design, Setting, and Participants: Randomized clinical trial conducted at 13 academic centers and clinical sites in China from April 2010 to December 2016 and final date of follow-up was April 30, 2020. Patients (n = 443) had early-stage triple-negative breast cancer and had completed standard adjuvant chemotherapy. Interventions: Eligible patients were randomized 1:1 to receive capecitabine (n = 222) at a dose of 650 mg/m2 twice a day by mouth for 1 year without interruption or to observation (n = 221) after completion of standard adjuvant chemotherapy. Main Outcomes and Measures: The primary end point was disease-free survival. Secondary end points included distant disease-free survival, overall survival, locoregional recurrence-free survival, and adverse events. Results: Among 443 women who were randomized, 434 were included in the full analysis set (mean [SD] age, 46 [9.9] years; T1/T2 stage, 93.1%; node-negative, 61.8%) (98.0% completed the trial). After a median follow-up of 61 months (interquartile range, 44-82), 94 events were observed, including 38 events (37 recurrences and 32 deaths) in the capecitabine group and 56 events (56 recurrences and 40 deaths) in the observation group. The estimated 5-year disease-free survival was 82.8% in the capecitabine group and 73.0% in the observation group (hazard ratio [HR] for risk of recurrence or death, 0.64 [95% CI, 0.42-0.95]; P = .03). In the capecitabine group vs the observation group, the estimated 5-year distant disease-free survival was 85.8% vs 75.8% (HR for risk of distant metastasis or death, 0.60 [95% CI, 0.38-0.92]; P = .02), the estimated 5-year overall survival was 85.5% vs 81.3% (HR for risk of death, 0.75 [95% CI, 0.47-1.19]; P = .22), and the estimated 5-year locoregional recurrence-free survival was 85.0% vs 80.8% (HR for risk of locoregional recurrence or death, 0.72 [95% CI, 0.46-1.13]; P = .15). The most common capecitabine-related adverse event was hand-foot syndrome (45.2%), with 7.7% of patients experiencing a grade 3 event. Conclusions and Relevance: Among women with early-stage triple-negative breast cancer who received standard adjuvant treatment, low-dose capecitabine maintenance therapy for 1 year, compared with observation, resulted in significantly improved 5-year disease-free survival. Trial Registration: ClinicalTrials.gov Identifier: NCT01112826.

10.
Clin Transl Med ; 10(7): e221, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33252851

RESUMO

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is an aggressive subtype of lymphoma, and multiple extranodal involvement (ENI) indicates adverse clinical outcomes. The aim of this study was to investigate the influence of oncogenic mutations and tumor microenvironment alterations on ENI in DLBCL. METHODS: The clinical features of 1960 patients with newly diagnosed DLBCL were analyzed, and DNA and RNA sequencing was performed on 670 and 349 patients, respectively. Oncogenic mutations and tumor microenvironment alterations were compared according to ENI and evaluated in zebrafish patient-derived tumor xenograft models. RESULTS: Multiple ENI was significantly associated with poor performance status, advanced stage, elevated serum lactate dehydrogenase, low response rate, and inferior prognosis. Lymphoma invasion of the bones, spleen, bone marrow, liver, and central nervous system were independent unfavorable prognostic factors. MYD88 was frequently mutated in patients with multiple ENI, co-occurred with mutations in CD79B, PIM1, TBL1XR1, BTG1, MPEG1, and PRDM1, and correlated with invasion of the bones, kidney/adrenal glands, breasts, testes, skin, and uterus/ovaries. For tumor microenvironment alterations, patients with multiple ENI showed higher regulatory T-cell (Treg)-recruiting activity, but lower extracellular matrix-encoding gene expression, than those without ENI and with single ENI. Elevated Treg-recruiting activity was related to mutations in B2M, SGK1, FOXO1, HIST1H1E, and ARID1A, and correlated with invasion of the bone marrow and thyroid. Additionally, mutations in MYD88, PIM1, TBL1XR1, SGK1, FOXO1, HIST1H1E, and ARID1A were associated with decreased major histocompatibility complex class I expression. Zebrafish models further revealed relationships between MYD88 mutations and invasion of the kidneys and gonads, as well as B2M mutations and invasion of the bone marrow. Increased CXCR4 expression is linked to bone marrow invasion in an organotropic way. CONCLUSIONS: Our findings thus contribute to an improved understanding of the biological behavior of multiple ENI and provide a clinical rationale for targeting ENI in DLBCL.

11.
Proc IEEE Int Symp Biomed Imaging ; 2020: 288-291, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33173559

RESUMO

Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.

12.
Artigo em Inglês | MEDLINE | ID: mdl-33108295

RESUMO

Although remarkable progress has been made on single-image super-resolution (SISR), deep learning methods cannot be easily applied to real-world applications due to the requirement of its heavy computation, especially for mobile devices. Focusing on the fewer parameters and faster inference SISR approach, we propose an efficient and time-saving wavelet transform-based network architecture, where the image super-resolution (SR) processing is carried out in the wavelet domain. Different from the existing methods that directly infer high-resolution (HR) image with the input low-resolution (LR) image, our approach first decomposes the LR image into a series of wavelet coefficients (WCs) and the network learns to predict the corresponding series of HR WCs and then reconstructs the HR image. Particularly, in order to further enhance the relationship between WCs and image deep characteristics, we propose two novel modules [wavelet feature mapping block (WFMB) and wavelet coefficients reconstruction block (WCRB)] and a dual recursive framework for joint learning strategy, thus forming a WCs prediction model to realize the efficient and accurate reconstruction of HR WCs. Experimental results show that the proposed method can outperform state-of-the-art methods with more than a 2x reduction in model parameters and computational complexity.

13.
BMJ Open ; 10(10): e036643, 2020 10 10.
Artigo em Inglês | MEDLINE | ID: mdl-33039992

RESUMO

INTRODUCTION: The ideal treatment for idiopathic granulomatous mastitis (IGM) remains unclear. In a prospective, single-centre, pilot study, we reported that ductal lavage treatment for non-lactational mastitis patients had a 1-year clinical complete response (cCR) rate of >90%, without any significant adverse events. Thus, in this multicentre, randomised, open-label, non-inferiority trial, we will aim to compare the effectiveness and safety of ductal lavage vs oral corticosteroids as the first-line treatment for patients with IGM. METHODS AND ANALYSIS: The trial will be conducted at the Breast Tumor Center of Sun Yat-sen Memorial Hospital in China and at least at one participating regional centre. We plan to recruit 140 eligible IGM patients who will be randomised into the ductal lavage group or oral corticosteroid group with a 1:1 ratio. The patients in the oral corticosteroid group will receive meprednisone or prednisone for 6 months. The patients in the ductal lavage group will receive ductal lavage and breast massage, as previously reported. All the participants will be followed up at the clinic for 1 year post randomisation. The primary endpoint of this trial will be the 1-year cCR rate, and the secondary endpoints will include the time to cCR, treatment failure rate, relapse rate and protocol compliance rate. The trial was designed to determine whether ductal lavage is non-inferior to oral corticosteroids (1-year cCR rate assumed to be 90%), with a non-inferiority margin of 15%. ETHICS AND DISSEMINATION: The ethics committee of Sun Yat-sen Memorial Hospital at Sun Yat-sen University approved the study (2018-Lun-Shen-Yan-No. 30). The results of the trial will be communicated to the participating primary care practices, published in international journals and presented at international clinical and scientific conferences. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT03724903); Pre-results.

14.
Anal Chem ; 92(20): 13971-13979, 2020 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-32970421

RESUMO

Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.

15.
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi ; 34(9): 1114-1119, 2020 Sep 15.
Artigo em Chinês | MEDLINE | ID: mdl-32929903

RESUMO

Objective: To assess the effectiveness of lateral ligament reconstruction with autogenous partial peroneus longus tendon for chronic lateral ankle instability. Methods: Between September 2014 and November 2018, 32 patients (32 sides) with chronic lateral ankle instability were treated with lateral ankle ligament reconstruction by using autogenous anterior half of the peroneus longus tendon. There were 25 males and 7 females, with an average age of 28.5 years (range, 20-51 years). The disease duration was 6-41 months (mean, 8.9 months). The preoperative Karlsson-Peterson ankle score was 53.7±9.7. The talar tilt angle was (14.9±3.7)°, and the anterior talar translation was (8.2±2.8) mm. Six patients combined with osteochondral lesion of talus and 4 patients combined with bony impingement. Results: All incisions healed by first intention postoperatively. All patients were followed up 12-53 months (mean, 22.7 months). At last follow-up, the Karlsson-Peterson ankle score was 85.2±9.6; the talar tilt angle was (4.3±1.4)°; the anterior talar translation was (3.5±1.1) mm. There were significant differences in all indexes between pre- and post-operation ( P<0.05). Seventeen patients were very satisfied with the results, 10 patients were satisfied, 4 patients were normal, and 1 patient was unsatisfied. After operation, the ankle sprain occurred in 7 cases, the tenderness around the compression screws at calcaneus in 5 cases, the anterolateral pain of ankle joint over 6 months in 4 cases. No patient had discomfort around the reciepient sites. At last follow-up, the ultrasonography examination showed that there was no significant difference in the density and diameter between bilateral peroneus longus tendons in 12 cases. Conclusion: For chronic lateral ankle instability, the lateral ankle ligament reconstruction with the autogenous partial peroneus longus tendon is a safe and effective surgical option.


Assuntos
Instabilidade Articular , Ligamentos Laterais do Tornozelo , Adulto , Tornozelo , Articulação do Tornozelo , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Tendões , Adulto Jovem
16.
Diagnostics (Basel) ; 10(9)2020 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-32957724

RESUMO

Dental implant surgery is a common treatment for missing teeth. Its survival rate is considerably affected by host bone quality and quantity, which is often assessed prior to surgery through dental cone-beam computed tomography (CBCT). Dental CBCT was used in this study to evaluate dental implant sites for (1) differences in and (2) correlations between cancellous bone density and cortical bone thickness among four regions of the jawbone. In total, 315 dental implant sites (39 in the anterior mandible, 42 in the anterior maxilla, 107 in the posterior mandible, and 127 in the posterior maxilla) were identified in dental CBCT images from 128 patients. All CBCT images were loaded into Mimics 15.0 to measure cancellous bone density (unit: grayscale value (GV) and cortical bone thickness (unit: mm)). Differences among the four regions of the jawbone were evaluated using one-way analysis of variance and Scheffe's posttest. Pearson coefficients for correlations between cancellous bone density and cortical bone thickness were also calculated for the four jawbone regions. The results revealed that the mean cancellous bone density was highest in the anterior mandible (722 ± 227 GV), followed by the anterior maxilla (542 ± 208 GV), posterior mandible (535 ± 206 GV), and posterior maxilla (388 ± 206 GV). Cortical bone thickness was highest in the posterior mandible (1.15 ± 0.42 mm), followed by the anterior mandible (1.01 ± 0.32 mm), anterior maxilla (0.89 ± 0.26 mm), and posterior maxilla (0.72 ± 0.19 mm). In the whole jawbone, a weak correlation (r = 0.133, p = 0.041) was detected between cancellous bone density and cortical bone thickness. Furthermore, except for the anterior maxilla (r = 0.306, p = 0.048), no correlation between the two bone parameters was observed (all p > 0.05). Cancellous bone density and cortical bone thickness varies by implant site in the four regions of the jawbone. The cortical and cancellous bone of a jawbone dental implant site should be evaluated individually before surgery.

17.
IEEE Trans Med Imaging ; PP2020 Sep 11.
Artigo em Inglês | MEDLINE | ID: mdl-32915732

RESUMO

In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images. Since there currently lack methods particularly for UDA instance segmentation, we first design a Domain Adaptive Mask R-CNN (DAM) as the baseline, with cross-domain feature alignment at the image and instance levels. In addition to the image-and instance-level domain discrepancy, there also exists domain bias at the semantic level in the contextual information. Next, we, therefore, design a semantic segmentation branch with a domain discriminator to bridge the domain gap at the contextual level. By integrating the semantic-and instance-level feature adaptation, our method aligns the cross-domain features at the panoptic level. Third, we propose a task re-weighting mechanism to assign trade-off weights for the detection and segmentation loss functions. The task re-weighting mechanism solves the domain bias issue by alleviating the task learning for some iterations when the features contain source-specific factors. Furthermore, we design a feature similarity maximization mechanism to facilitate instance-level feature adaptation from the perspective of representational learning. Different from the typical feature alignment methods, our feature similarity maximization mechanism separates the domain-invariant and domain-specific features by enlarging their feature distribution dependency. Experimental results on three UDA instance segmentation scenarios with five datasets demonstrate the effectiveness of our proposed PDAM method, which outperforms state-of-the-art UDA methods by a large margin.

18.
Artigo em Inglês | MEDLINE | ID: mdl-32946382

RESUMO

Learning to improve AUC performance for imbalanced data is an important machine learning research problem. Most methods of AUC maximization assume that the model function is linear in the original feature space. However, this assumption is not suitable for nonlinear separable problems. Although there have been several nonlinear methods of AUC maximization, scaling up nonlinear AUC maximization is still an open question. To address this challenging problem, in this paper, we propose a novel large-scale nonlinear AUC maximization method (named as TSAM) based on the triply stochastic gradient descents. Specifically, we first use the random Fourier feature to approximate the kernel function. After that, we use the triply stochastic gradients w.r.t. the pairwise loss and random feature to iteratively update the solution. Finally, we prove that TSAM converges to the optimal solution with the rate of O(1/t) after t iterations. Experimental results on a variety of benchmark datasets not only confirm the scalability of TSAM, but also show a significant reduction of computational time compared with existing batch learning algorithms, while retaining the similar generalization performance.

19.
Artigo em Inglês | MEDLINE | ID: mdl-32845848

RESUMO

Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art approach for active learning. Fully utilizing a large amount of unlabeled data provides a second chance to improve the performance of active learning. Although there have been several active learning methods proposed by combining with semisupervised learning, fast active learning with fully exploiting unlabeled data and querying discriminative and representative samples is still an open question. To overcome this challenging issue, in this article, we propose a new efficient batch mode active learning algorithm. Specifically, we first provide an active learning risk bound by fully considering the unlabeled samples in characterizing the informativeness and representativeness. Based on the risk bound, we derive a new objective function for batch mode active learning. After that, we propose a wrapper algorithm to solve the objective function, which essentially trains a semisupervised classifier and selects discriminative and representative samples alternately. Especially, to avoid retraining the semisupervised classifier from scratch after each query, we design two unique procedures based on the path-following technique, which can remove multiple queried samples from the unlabeled data set and add the queried samples into the labeled data set efficiently. Extensive experimental results on a variety of benchmark data sets not only show that our algorithm has a better generalization performance than the state-of-the-art active learning approaches but also show its significant efficiency.

20.
Artigo em Inglês | MEDLINE | ID: mdl-32823531

RESUMO

Satisfactory host bone quality and quantity promote greater primary stability and better osseointegration, leading to a high success rate in the use of dental implants. However, the increase in life expectancy as a result of medical advancements has led to an aging population, suggesting that osteoporosis may become a problem in clinical dental implant surgery. Notably, relative to the general population, bone insufficiency is more common in women with post-menopausal osteoporosis. The objective of this study was to compare the thickness of the crestal cortical bone at prospective dental implant sites between menopausal and non-menopausal women. Prospective dental implant sites in the jawbone were evaluated in two groups of women: a younger group (<50 years old), with 149 sites in 48 women, and an older group (>50 years old) with 191 sites, in 37 women. The thickness of the crestal cortical bone at the dental implant site was measured based on each patient's dental cone-beam computed tomography images. For both groups, one-way analysis of variance and Tukey's post-test were used to assess the correlation between cortical bone thickness and the presence of implants in the four jawbone regions. Student's t-test was further used to compare differences between the older and younger groups. From the retrospective study results, for both groups, thickness of the crestal cortical bone was the highest in the posterior mandible, followed by anterior mandible, anterior maxilla, and posterior maxilla. Compared with the younger group, the older group had a lower mean thickness of the crestal cortical bone. Among the four regions, however, only in the posterior maxilla was the crestal cortical bone significantly thinner in the older group than in the younger group.

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