Due to the complex mechanism of ankle injury, the clinical diagnosis of ankle fracture is extremely difficult. In order to simplify the fracture diagnosis process, this study proposes an automatic diagnosis model of ankle fractures. Firstly, an ankle fracture classification method suitable for machine learning was developed. By dividing six fracture regions, multiple types of fractures were clarified, and a corresponding dataset was created accordingly. Secondly, the random forest model was used to preprocess the X‐ray images and segment the fracture foreground part of the X‐ray images. Finally, the bag of visual words (BoVW) was used for feature extraction, and classifiers were constructed in different regions for classification. The area under the curve (AUC) of XGBoost was 0.92 ± 0.06. The performance of the XGBoost has proved to be better compared with the SVM when training on a small dataset in each region.
Background The introduction of fracture reduction robot can solve the problem of large reduction forces during fracture reduction surgeries and the need to collect multiple medical images. However, because its safety has not been certified, there are few academic achievements on this type of robot. To calculate the safety factor during its operation, a musculoskeletal model needs to be established to study the constraints of muscles on the robot. The existing academic achievements of musculoskeletal modelling are mainly for application such as rehabilitation treatment and collision in car accidents. Methods A musculoskeletal model applied to the fracture reduction robot is proposed in this paper. First, by comparing the characteristics of mainstream muscle models and combining the biological characteristics of the anesthetised muscles, the Hill model was selected as the muscle model for this study. Second, based on the motion composition of six spatial degrees of freedom, five basic fractural malposition situations are proposed. Then, a 170‐cm tall male musculoskeletal model was built in Opensim. Based on this model, the muscle force curves of the above malposition situations are calculated. Finally, a similar musculoskeletal model was established in Adams, and the accuracy of its muscle force data was tested. The study is approved by the ethics committee of the Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, Beijing, China. Results The muscle force curve of Opensim and Adams model under situations of five basic malposition are compared. Most of the correlation coefficients are in the range of 0.98–0.99. The overall correlation coefficient is greater than 0.95. Conclusions The simulation results prove that this model can be used for the safety assessment of the fracture reduction robots. This model will be served as an environmental constraint to study the control of fracture reduction robot.
Background: Human body is an integrated system of bones and muscles. To date, there are insufficient studies on the effect of muscles on the trajectory planning of orthopaedic robot. To this end, based on a Stewart-Gough platform (6-UPU) fracture reduction orthopaedic robot, a musculoskeletal trajectory optimisation method was constructed for the interference of soft tissue during the reduction process.Methods: Firstly, pose description of the fracture reduction orthopaedic robot was introduced, and its working space was analysed. Secondly, an improved Hill muscle theory was used to construct the musculoskeletal system, and finite element analysis (FEA) was carried out. Thirdly, particle swarm optimisation (PSO) with variable weights was imported, and fracture reduction trajectory planning was obtained by quintic polynomial in the workspace. Then mathematical model for trajectory optimisation was presented, and targets of musculoskeletal optimisation were extracted, which include muscle energy consumption, robot-assisted repositioning time and trajectory length. In this sense, musculoskeletal integration trajectory optimisation method was put forward. Finally, comparative optimisation simulation with skeleton only and bone and muscle together were tested, and safety experiment with musculoskeletal integration was conducted.Results: A cone shape workspace was got, whose range can be defined as 635.14 mm on the X axis, 720 mm on the Y axis, and 240 mm on the Z axis, respectively. Besides, different FEA displacements revealed the effect of bone and muscle on the movement of the robot. Moreover, the optimal results with and without muscle had showed different movement time: the former consumed about 27 s, but the latter spent about 25 s. Additionally, the speed and acceleration of the drive rods can be obtained from zero to zero during the reset. Conclusions:The results show that the optimised trajectory obtained with this method is safe and reliable. Furthermore, the presence of muscle has great influence on the trajectory of robot, which could prolong the reduction time so as to prevent the occurrence of uneven soft and hard traction rate problem. This paper could be helpful for the future trajectory planning study of fracture reduction orthopaedic robot.
BACKGROUND Although the chemical components of basal, reflex, and emotional tears are different, the presence of distinctions in the tears of different emotions is still unknown. The present study aimed to address the biochemical basis behind emotional tears through non-targeted metabolomics analysis between positive and negative emotional tears of humans. METHODS Samples of reflex (C), negative (S), and positive (M) emotional tears were collected from healthy college participants. Untargeted metabolomics was performed to identify the metabolites in the different types of tears. The differentially altered metabolites were screened and assessed using univariate and multivariate analyses. RESULTS The global metabolomics signatures classified the C, S, and M emotional tears. A total of 133 significantly differential metabolites of ESI- mode were identified between negative and positive emotional tears. The top 50 differential metabolites between S and M were highly correlated. The significantly altered pathways included porphyrin & chlorophyll metabolism, bile secretion, biotin metabolism, arginine & proline metabolism and among others. CONCLUSION The metabolic profiles between reflex, positive, and negative emotional tears of humans are distinct. Secretion of positive and negative emotional tears are distinctive biological activities. Therefore, the present study provides a chemical method to detect human emotions which may become a powerful tool for diagnosis of mental disease and identification of fake tears.
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