Objective To develop a Matlab toolbox to improve the efficiency of musculoskeletal kinematics analysis while ensuring the consistency of musculoskeletal kinematics analysis process and results. Methods Adopted the design concept of “Batch processing tedious operation”, based on the Matlab connection OpenSim interface function ensures the consistency of musculoskeletal kinematics analysis process and results, the functional programming was applied to package the five steps for scale, inverse kinematics analysis, residual reduction algorithm, static optimization analysis, and joint reaction analysis of musculoskeletal kinematics analysis as functional functions, and command programming was applied to analyze musculoskeletal movements in large numbers of patients. A toolbox called LLMKA (Lower Limbs Musculoskeletal Kinematics Analysis) was developed. Taking 120 patients with medial knee osteoarthritis as the research object, a clinical researcher was selected using the LLMKA toolbox and OpenSim to test whether the analysis process and results were consistent between the two methods. The researcher used the LLMKA toolbox again to conduct musculoskeletal kinematics analysis in 120 patients to verify whether the use of this toolbox could improve the efficiency of musculoskeletal kinematics analysis compared with using OpenSim. Results Using the LLMKA toolbox could analyze musculoskeletal kinematics analysis in a large number of patients, and the analysis process and results were consistent with the use of OpenSim. Compared to using OpenSim, musculoskeletal kinematics analysis was completed in 120 patients using the LLMKA toolbox with only 2 operations were needed to enter the patient body mass data, operating steps decreased by 99.19%, total analysis time by 66.84%, and manual participation time by 99.72%, just need 0.079 1 hour (4 minutes and 45 seconds). Conclusion The LLMKA toolbox can complete a large number of musculoskeletal kinematics analysis in patients with one click in a way that is consistent in process and results with using OpenSim, reducing the total time of musculoskeletal kinematics analysis, and liberating clinical researchers from cumbersome steps, making more energy into the clinical significance of musculoskeletal kinematics analysis results.
ObjectiveTo observe the interobserver agreement of classification of macular degeneration in severe pathological myopia (PM) by ophthalmologists with different clinical experience. MethodsA retrospective study. From January 2019 to December 2021, 171 eyes of 102 patients with severe PM macular degeneration who were examined at Eye Center of Beijing Tongren Hospital of Capital Medical University were included in the study. The clinical data such as age, gender, axial length, spherical equivalent power, fundus color photography, and optical coherence tomography (OCT) were collected in detail. Six independent ophthalmologists (A, B, C, D, E, F) classified each fundus photography based on META-PM and ATN classification of atrophy (A) system and interobserver agreement was assessed by Kappa statistics. According to the classification standard of traction (T) in the ATN classification, the OCT images were interpreted and classified, in which T0 was subdivided into retinal pigment epithelium (RPE) and choroidal thinning, choroidal neovascularization (CNV) with partial RPE and choroidal atrophy, RPE, and choroidal atrophy. Lamellar macular hole can't be classified by ATN system, which was defined as TX. Kappa (κ) test was used to analyze the consistency of classification results between physicians A, B, C, D, E and F. κ value ≤0.4 indicates low consistency, 0.4＜κ value ≤ 0.6 indicates moderate consistency, and κ value ＞0.6 indicates strong consistency. ResultsAmong the 171 eyes of 102 cases, there were 20 males with 37 eyes (19.6%, 20/102), and 82 females with 134 eyes (80.4%, 82/102); age was 61.97±8.78 years; axial length was (30.87±1.93) mm; equivalent spherical power was (－16.56±7.00) D. Atrophy (A) classification results in META-PM classification and ATN classification, the consistency of physician A, B, C, D, E and physician F were 73.01%, 77.19%, 81.28%, 81.28%, 88.89%; κ value were 0.472, 0.538, 0.608, 0.610, 0.753, respectively. In the ATN classification, the T0, T1, T2, T3, T4, and T5 were in 109, 18, 11, 12, 9, and 8 eyes, respectively; TX was in 4 eyes. ConclusionsThere are differences in the consistency of classification of severe PM macular lesions among physicians with different clinical experience, and the consistency will gradually improve with the accumulation of clinical experience.
Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.
本文针对二分类变量结局指标相对（而非绝对）治疗效果的不一致性。证据本身不会因不同研究结果具有一致性而升级，但可能因不一致而降低质量级别。衡量一致性的标准包括点估计值的相似性、可信区间的重叠程度以及统计学判定标准包括异质性检验和I2。系统评价作者应提出并检验少数几个与患者、干预措施、结局指标以及方法学相关的先验假设以探寻异质性来源。当不一致性很大且无法解释时，因不一致性而降低质量级别是恰当的，特别当某些研究显示有显著益处而其他显示无益甚至有害时（而非仅是疗效大与疗效小的比较）。明显的亚组效应可能不可靠。如果亚组效应满足以下条件，其可信度将会增加：基于少数几个有具体方向的先验假设、亚组比较来自研究内而非研究间、交互检验的P值小、结果有生物学意义。
目的 利用局部一致性（ReHo）方法探测创伤后应激障碍（PTSD）患者在静息状态下是否存在着大脑功能异常。 方法 2010年5月－7月对18例未经治疗的地震PTSD患者和19例同样经历地震但未患PTSD的对照者进行了静息态功能磁共振成像（Rs-fMRI） 扫描。应用ReHo方法处理Rs-fMRI数据，得出PTSD患者的异常脑区，并将患者存在组间差异的脑区ReHo值与临床用PTSD诊断量表（CAPS）、汉密尔顿抑郁量表（HAMD）和汉密尔顿焦虑量表（HAMA）分别进行相关分析。 结果 ① PTSD组ReHo显著增加的脑区包括右侧颞下回、楔前叶、顶下叶、中扣带回，左侧枕中回以及左/右侧后扣带回；ReHo显著降低的脑区包括左侧海马和左/右侧腹侧前扣带回。② 异常脑区中后扣带回和右侧中扣带回ReHo与HAMD呈负相关（中扣带回r=?0.575，P=0.012；右侧后扣带回：r=?0.507，P=0.032），其余脑区ReHo与临床指标无明显相关性（P＞0.05），左侧海马与CAPS的相关性相对其他脑区较大（r=?0.430，P=0.075）。 结论 PTSD患者在静息状态下即存在着局部脑功能活动的降低和增加，ReHo方法可能有助于研究PTSD患者静息状态脑活动。
Objective To validate the effectiveness of a novel comprehensive classification for intertrochanteric fracture (ITF). MethodsThe study included 616 patients with ITF, including 279 males (45.29%) and 337 females (54.71%); the age ranged from 23 to 100 years, with an average of 72.5 years. Two orthopaedic residents (observers Ⅰ and Ⅱ) and two senior orthopaedic surgeons (observers Ⅲ and Ⅳ) were selected to classify the CT imaging data of 616 patients in a random order by using the AO/Orthopaedic Trauma Association (AO/OTA) classification of 1996/2007 edition, the AO/OTA classification of 2018 edition, and the novel comprehensive classification method at an interval of 1 month. Kappa consistency test was used to evaluate the intra-observer and inter-observer consistency of the three ITF classification systems. ResultsThe inter-observer consistency of the three classification systems evaluated by 4 observers twice showed that the 3 classification systems had strong inter-observer consistency. Among them, the κ value of the novel comprehensive classification was higher than that of the AO/OTA classification of 1996/2007 edition and 2018 edition, and the experience of observers had a certain impact on the classification results, and the inter-observer consistency of orthopaedic residents was slightly better than that of senior orthopaedic surgeons. The intra-observer consistency of two evaluations of three classification systems by 4 observers showed that the consistency of the novel comprehensive classification was better for the other 3 observers, except that the consistency of observer Ⅳ in the AO/OTA classification of 2018 version was slightly higher than that of the novel comprehensive classification. The results showed that the novel comprehensive classification has higher repeatability, and the intra-observer consistency of senior orthopaedic surgeons was better than that of orthopaedic residents. ConclusionThe novel comprehensive classification system has good intra- and inter-observer consistency, and has high validity in the classification of CT images of ITF patients; the experience of observers has a certain impact on the results of the three classification systems, and those with more experiences have higher intra-observer consistency.
The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.
A great number of studies have demonstrated functional abnormalities in children with attention-deficit/hyperactivity disorder (ADHD), although conflicting results have also been reported. And few studies analyzed homotopic functional connectivity between hemispheres. In this study, resting-state functional magnetic resonance imaging (MRI) data were recorded from 45 medication-naïve ADHD children and 26 healthy controls. The regional homogeneity (ReHo), degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) values were compared between the two groups to depict the intrinsic brain activities. We found that ADHD children exhibited significantly lower ReHo and DC values in the right middle frontal gyrus and the two values correlated with each other; moreover, lower VMHC values were found in the bilateral occipital lobes of ADHD children, which was negatively related with anxiety scores of Conners' Parent Rating Scale (CPRS-R) and positively related with completed categories of Wisconsin Card Sorting Test (WCST). Our results might suggest that less spontaneous neuronal activities of the right middle frontal gyrus and the bilateral occipital lobes in ADHD children.
Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.