Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.
Objective To evaluate the effect of optimizing the management measures of cataract ambulatory surgery. Methods The patients who underwent cataract phacoemulsification combined with intraocular lens implantation in the Ambulatory Surgery Center of East District of Beijing Tongren Hospital affiliated to Capital Medical University were selected. Patients between January and December 2021 (after the optimization of ambulatory surgery process) were included, and patients between January and December 2020 (before the optimization of ambulatory surgery process) were included as control. The three factors of age, gender and surgical eye type were used as predictive variables for propensity score matching. The proportion of patients who completed the surgery according to the scheduled time, the proportion of eye drops used according to the doctor’s instructions and the number of hospital visits before and after the optimization of the ambulatory surgery process were compared with the patients who successfully matched. Results A total of 28306 patients were included, including 13284 before and 15022 after process optimization. There were 13467 males and 14839 females, with a median age of 70 (60, 78) years. There was no statistically significant difference in the age of patients before the process optimization (P>0.05), but there was statistically significant difference in gender and surgical eye (P<0.05). After the propensity score matching, a total of 12932 pairs of patients were matched successfully. After the propensity score matching, there was no statistically significant difference between the two groups in age, gender and surgical eye (P>0.05). After the process optimization, the proportion of patients who completed surgery on schedule (98.8% vs. 93.3%) and used eyedrops according to the doctor’s instructions (97.4% vs. 93.0%) were higher than that before the process optimization, and the proportion of patients who came to hospital more than 3 times (0.7% vs. 1.9%) was lower than that before the process optimization (P<0.05). Conclusion The optimized ambulatory surgery process can help patients complete the surgery according to the scheduled time and use eye medication according to the doctor’s instructions, and can reduce the number of patients coming to the hospital.
High quality clinical trial depends on the preliminary research design and optimizing, the quality control in the medium term, the source data verification and statistics in later stages. Steering committee (SC) can meet above requirements. According to the characteristics of the research project, we invited international experts whose professional background are obstetrics and gynecology, statistics and methodology to set up SC. SC will hold regular conference, the content of the conference included project progress and quality control, research assistant training and assessment, conducting knowledge lectures and so on. The establishment of SC ensured the protocol maneuverability, answered PCOS related problems from different professional perspectives, and solved the problems such as how to improve the scientific research output. At the same time, it provides a platform of scientific research practice and self-improvement. It has profound influence on standardizing the management of the clinical trial, strengthening the consciousness of team work, promoting multi-disciplinary team cooperation, expanding scientific research thinking and cultivating clinical research talent.
Percutaneous pulmonary puncture guided by computed tomography (CT) is one of the most effective tools for obtaining lung tissue and diagnosing lung cancer. Path planning is an important procedure to avoid puncture complications and reduce patient pain and puncture mortality. In this work, a path planning method for lung puncture is proposed based on multi-level constraints. A digital model of the chest is firstly established using patient's CT image. A Fibonacci lattice sampling is secondly conducted on an ideal sphere centered on the tumor lesion in order to obtain a set of candidate paths. Finally, by considering clinical puncture guidelines, an optimal path can be obtained by a proposed multi-level constraint strategy, which is combined with oriented bounding box tree (OBBTree) algorithm and Pareto optimization algorithm. Results of simulation experiments demonstrated the effectiveness of the proposed method, which has good performance for avoiding physical and physiological barriers. Hence, the method could be used as an aid for physicians to select the puncture path.
In this paper, a method for dose calculation with pencil beam kernels constructed by point kernel superposition was proposed to accelerate the dose calculation during intensity optimization iteration. With this method, the direct aperture optimization method can be integrated in the planning system based on point kernel convolution/superposition model. The dose calculation time was also reduced during the iteration. From the result of the phantom and clinical patient data test, it was concluded that this method could be used for the intensity optimization of iteration dose calculation as the satisfied precision due to the optimization result coherence obtained. By implementing the method in the planning system product based on point kernel convolution/superposition model, a lot of additional research and development works for the pencil beam dose calculation model as well as the product maintenance cost can be avoided.
Hemolysis is one of the main complications associated with the use of ventricular assist devices. The primary factors influencing hemolysis include the shear stress and exposure time experienced by red blood cells. In addition, factors such as local negative pressure and temperature may also impact hemolysis. The different combinations of hemolysis prediction models and their empirical constants lead to significant variations in prediction results; compared to the power-law model, the OPO model better accounts for the complexity of turbulence. In terms of improving hemolytic performance, research has primarily focused on optimizing blood pump structures, such as adjustments to pump gaps, impellers, and guide vanes. A small number of scholars have studied hemolytic performance through control modes of blood pump speed and the selection of blood-compatible materials. This paper reviews the main factors influencing hemolysis, prediction methods, and improvement strategies for rotary blood pumps, which are currently the most widely used. It also discusses the limitations in current hemolysis research and provides an outlook on future research directions.
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.
The goal of this paper is to solve the problems of large volume, slow dynamic response and poor intelligent controllability of traditional gait rehabilitation training equipment by using the characteristic that the shear yield strength of magnetorheological fluid changes with the applied magnetic field strength. Based on the extended Bingham model, the main structural parameters of the magnetorheological fluid damper and its output force were simulated and optimized by using scientific computing software, and the three-dimensional modeling of the damper was carried out after the size was determined. On this basis and according to the design and use requirements of the damper, the finite element analysis software was used for force analysis, strength check and topology optimization of the main force components. Finally, a micro magnetorheological fluid damper suitable for wearable rehabilitation training system was designed, which has reference value for the design of lightweight, portable and intelligent rehabilitation training equipment.
A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.
The nondestructive reconstruction of three-dimensional (3D) temperature field in biological tissue is always an important problem to be resolved in biomedical engineering field. This paper presents a novel method of nondestructive reconstruction of 3D temperature field in biological tissue based on multi-island genetic algorithm (MIGA). By this method, the resolving of inverse problem of bio-heat transfer is transformed to be a solving process of direct problem. An experiment and its corresponding simulation were carried out to verify the feasibility and reliability. In the experiment a high purity polypropylene material, whose thermophysical parameters were similar to the fat tissue being tested, were adopted so that it could avoid the negative results created by the other factors. We set the position P(x, y, z) as the point heat source in the biological tissue and its temperature t as optimization variable, got the experimental temperature values of the points in a module surface, subtracted them from the corresponding simulating temperature values in the same module surface, and then took the sum of absolute value. We took it as the objective function of successive iteration. It was found that the less the target value was, the more optimal the current variables, i.e. the heat source position and the temperature values, were. To improve the optimization efficiency, a novel establishment method of objective function was also provided. The simulating position and experimental position of heat source were very approximate to each other. When the optimum values are determined, the corresponding 3D temperature field is also confirmed, and the temperature distribution of arbitrary section can be acquired. The MIGA can be well applied in the reconstruction of 3D temperature field in biological tissue. Because of the differences between the MIGA and the traditional numerical methods, we do not have to acquire all the data of surface. It is convenient and fast, and shows a prosperous application future.