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.
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.
ObjectivesTo analyze the application value of 6-minute walking test (6MWT) in the clinical evaluation of chronic heart failure (CHF).MethodsPubMed, EMbase, The Cochrane Library, CBM, VIP, WanFang Data and CNKI databases were searched online to collect randomized controlled trials (RCTs) of 6-minute walking distance (6MWD) as the CHF evaluation index. Two reviewers independently screened literature, extracted data, and then analyzed data by using SPSS 17.0 statistical software. The 6MWD with symptom, quality of life, exercise tolerance (ETT), left ventricular ejection fraction (LVEF), peak oxygen consumption (pVO2) were analyzed by Kappa consistency test, and the possible influencing factors of 6MWD were analyzed by logistic regression.ResultsA total of 158 RCTs involving 17 853 patients were included. The results of statistical analysis showed that: 6MWD was consistent with the improvement of symptoms, quality of life, ETT, LVEF and pVO2 (Kappa>0.4). Baseline 6MWD (OR=2.91, 95%CI 1.278 to 6.634,P=0.011) and NYHA Ⅲ-Ⅳ ratio (OR=2.59, 95%CI 1.091 to 6.138, P=0.031) were the independent influencing factors for 6MWD improvement separately.ConclusionsThe 6MWT is an objective and reliable indicator of CHF evaluation.
ObjectiveTo introduce sensitivity and homogeneity tests in network meta-analysis and its implementation in R software. MethodsUsing an example, we performed sensitivity analysis by comparing the random effect model with the fixed effect model. Homogeneity analysis was performed using metaphor package and combinat package in R software. ResultsThe results of the two models were similar, and the data was steady. The results of homogeneity analysis showed that the confidential intervals in all loops were crossed over with blank value; and direct and indirect estimates of the effects in network meta-analysis were not significantly different, with good homogeneity. ConclusionNetwork meta-analysis is a kind of indirect comparison analysis method, and its sensitivity is especially important. The introduction of homogeneity makes network meta-analysis more accurate. Using R software for sensitivity and homogeneity analysis in network meta-analysis is a feasible method.
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.
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.
This study sought to reveal the difference of brain functions at resting-state between subjects with sub-health and normal controls by using the functional magnetic resonance imaging (fMRI) technology. Resting-state fMRI scans were performed on 24 subjects of sub-health and on 24 healthy controls with gender, age and education matched with the sub-health persons. Compared to the healthy controls, the sub-health group showed significantly higher regional homogeneity (ReHo) in the left post-central gyrus and the right post-central gyrus. On the other hand, the sub-health group showed significantly lower ReHo in the left superior frontal gyrus, in the right anterior cingulated cortex and ventra anterior cingulate gyrus, in the left dorsolateral frontal gyrus, and in the right middle temporal gyrus. The Significant difference in ReHo suggests that thebsub-health persons have abnormalities in certain brain regions. It is proved that its specific action and meaning deserves further assessment.
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。
Measurement properties studies of patient-reported outcome measures (PROMs) aims to validate the measurement properties of PROMs. In the process of designing and statistical analysis of these measurement properties studies, bias will occur if there are any defects, which will affect the quality of PROMs. Therefore, the COSMIN (consensus-based standards for the selection of health measurement instruments) team has developed the COSMIN risk of bias (COSMIN-RoB) checklist to evaluate risk of bias of studies on measurement properties of PROMs. The checklist can be used to develop systematic reviews of PROMs measurement properties, and for PROMs developers, it can also be used to guide the research design in the measurement tool development process for reducing bias. At present, similar assessment tools are lacking in China. Therefore, this article aims to introduce the primary contents of COSMIN-RoB checklist and to interpret how to evaluate risk of bias of the internal structure studies of PROMs with examples.