The kinematic model parameter deviation is the main factor affecting the positioning accuracy of neurosurgical robots. To obtain more realistic kinematic model parameters, this paper proposes an automatic parameters identification and accuracy evaluation method. First, an identification equation contains all robot kinematics parameter was established. Second, a multiple-pivot strategy was proposed to find the relationship between end-effector and tracking marker. Then, the relative distance error and the inverse kinematic coincidence error were designed to evaluate the identification accuracy. Finally, an automatic robot parameter identification and accuracy evaluation system were developed. We tested our method on both laboratory prototypes and real neurosurgical robots. The results show that this method can realize the neurosurgical robot kinematics model parameters identification and evaluation stably and quickly. Using the identified parameters to control the robot can reduce the robot relative distance error by 33.96% and the inverse kinematics consistency error by 67.30%.
Real-time continuous glucose monitoring can help diabetics to control blood sugar levels within the normal range. However, in the process of practical monitoring, the output of real-time continuous glucose monitoring system is susceptible to glucose sensor and environment noise, which will influence the measurement accuracy of the system. Aiming at this problem, a dual-calibration algorithm for the moving-window double-layer filtering algorithm combined with real-time self-compensation calibration algorithm is proposed in this paper, which can realize the signal drift compensation for current data. And a real-time continuous glucose monitoring instrument based on this study was designed. This real-time continuous glucose monitoring instrument consisted of an adjustable excitation voltage module, a current-voltage converter module, a microprocessor and a wireless transceiver module. For portability, the size of the device was only 40 mm × 30 mm × 5 mm and its weight was only 30 g. In addition, a communication command code algorithm was designed to ensure the security and integrity of data transmission in this study. Results of experiments in vitro showed that current detection of the device worked effectively. A 5-hour monitoring of blood glucose level in vivo showed that the device could continuously monitor blood glucose in real time. The relative error of monitoring results of the designed device ranged from 2.22% to 7.17% when comparing to a portable blood meter.
This study proposed a method to calibrate tube focus spot and the center plane of rotation in computed tomography system. In the method, the tube was rotated to 0° and 180° respectively, and then one metal jig with symmetric windows A and B was scanned at each position under the tube cool and static condition. According to the geometry of tube focus spot, aperture center of the collimator and jig, the distance between tube focus spot and the center plane of rotation were calculated with the X ray transmittance data after denoising, mean value and normalization. To verify the practicability and validity of the method, the tube focus spot in a 16 slices CT system (Brivo CT385, GE, China) was calibrated, and the result after calibration was validated by scanning a polaroid film. The validation result showed that the deviation between tube focal spot and center plane of rotation was 0.02 mm and was in the error range within ± 0.1 mm. The results of this study showed that, as a simple and low-cost design, the method could be used for fast calibration between tube focus spot and the center plane of rotation.
In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.
The accuracy of the clinical prediction model determines its extrapolation and application value. When the prediction model is applied to a new setting, the differences between the new population and the initially modeled population in terms of study time, population characteristics, region, and other factors could lead to a reduction in its predictive performance. Calibrating or updating the prediction model with appropriate statistical methods is important to improve the accuracy of the prediction model in new populations. The model updating methods mainly include regression coefficients updating, meta-model updating and dynamic model updating. However, due to the limitations of meta-model updating and dynamic model updating in practical applications, the regression coefficient updating method is still the most common method in model updating. This paper introducd several types of model updating methods, the regression coefficients updating methods for two common clinical prediction models based on Logistic regression and Cox regression, and provide corresponding R codes for reference of researchers.
Objective To investigate the prognostic value of preoperative inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and fibrinogen-to-prealbumin ratio (FPR), for postoperative survival in patients with resectable esophageal squamous cell carcinoma (ESCC). Additionally, to construct and validate a prognostic model for ESCC based on these inflammatory markers combined with TNM staging. Methods We retrospectively analyzed the clinical data of patients with histologically confirmed ESCC who underwent surgical resection at the First Affiliated Hospital of the University of Science and Technology of China during 2017. Receiver operating characteristic (ROC) curves were used to determine the optimal cut-off values for preoperative NLR, PLR, SII, and FPR. Clinicopathological characteristics were compared between patient groups with different levels of these markers. Survival analysis was performed using the Kaplan-Meier method, and univariate and multivariate regression analyses were conducted using the Cox proportional hazards model to identify prognostic factors. Nomograms for predicting overall survival (OS) and disease-free survival (DFS) were constructed using R software. The model's discrimination was assessed with ROC curves, its calibration was evaluated with calibration curves, and its clinical utility was determined by decision curve analysis (DCA). Results A total of 224 patients who underwent surgery for ESCC were included, comprising 180 males and 44 females. The optimal preoperative cut-off values of NLR, PLR, SII, and FPR for predicting postoperative OS were 2.70, 140.34, 360.73, and 0.015, respectively. The 5-year OS and DFS rates in the high-NLR group were lower than in the low-NLR group (both P<0.001). Similarly, patients in the high-PLR group (P=0.005 and P=0.009, respectively), high-SII group (P=0.008 and P=0.018, respectively), and high-FPR group (both P<0.001) had lower 5-year OS and DFS rates compared to their low-level counterparts. Multivariate Cox regression analysis revealed that patient age, T stage, N stage, tumor differentiation, and NLR>2.70 et al were independent prognostic factors for both OS and DFS. Based on these factors, nomograms for OS and DFS were constructed. The area under the ROC curve (AUC) for 3- and 5-year OS were 0.966 and 0.907, respectively, and for 3- and 5-year DFS were 0.960 and 0.919, respectively. The calibration curves showed good agreement between predicted and actual outcomes. DCA demonstrated that the models provided a positive net benefit for all patients under intervention. Conclusion Preoperative levels of NLR, PLR, SII, and FPR are associated with the prognosis of patients with ESCC, with NLR being an independent prognostic predictor. The nomogram models, constructed based on patient age, tumor differentiation, T stage, N stage, and preoperative NLR level, can accurately predict the prognosis of patients with ESCC. These models may help guide preoperative clinical decision-making and tailor treatment and follow-up strategies.
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.
Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome karyotyping. In this paper, we propose a model named segmentally recalibrated dense convolutional network (SR-DenseNet). In each stage of the model, the dense connected network layers is used to extract the features of different abstract levels of chromosomes automatically, and then the concatenation of all the layers which extract different local features is recalibrated with squeeze-and-excitation (SE) block. SE blocks explicitly construct learnable structures for importance of the features. Then a model fusion method is proposed and an expert group of chromosome recognition models is constructed. On the public available Copenhagen chromosome recognition dataset (G-bands) the proposed model achieves error rate of only 1.60%, and with model fusion the error further drops to 0.99%. On the Padova chromosome dataset (Q-bands) the model gets the corresponding error rate of 6.67%, and with model fusion the error further drops to 5.98%. The experimental results show that the method proposed in this paper is effective and has the potential to realize the automation of chromosome type recognition.
In order to calibrate the hand-eye transformation of the surgical robot and laser range finder (LRF), a calibration algorithm based on a planar template was designed. A mathematical model of the planar template had been given and the approach to address the equations had been derived. Aiming at the problems of the measurement error in a practical system, we proposed a new algorithm for selecting coplanar data. This algorithm can effectively eliminate considerable measurement error data to improve the calibration accuracy. Furthermore, three orthogonal planes were used to improve the calibration accuracy, in which a nonlinear optimization for hand-eye calibration was used. With the purpose of verifying the calibration precision, we used the LRF to measure some fixed points in different directions and a cuboid’s surfaces. Experimental results indicated that the precision of a single planar template method was (1.37±0.24) mm, and that of the three orthogonal planes method was (0.37±0.05) mm. Moreover, the mean FRE of three-dimensional (3D) points was 0.24 mm and mean TRE was 0.26 mm. The maximum angle measurement error was 0.4 degree. Experimental results show that the method presented in this paper is effective with high accuracy and can meet the requirements of surgical robot precise location.
Based on repeated experiments as well as continuous researching and improving, an efficient scheme to measure velocity and displacement of the coxa and knee movements based on video image processing technique is presented in this paper. The scheme performed precise and real-time quantitative measurements of 2D velocity or displacement of the coxa and knee using a video camera mounted on one side of the healing and training beds. The beds were based on simplified pinhole projection model. In addition, we used a special-designed auxiliary calibration target, composed by 24 circle points uniformly located on two concentric circles and two straight rods which can rotate freely along the concentric center within the vertical plane, to do the measurements. Experiments carried out in our laboratory showed that the proposed scheme could basically satisfy the requirements about precision and processing speed of such kind of system, and would be very suitable to be applied to smart evaluation/training and healing system for muscles/balance function disability as an advanced and intuitional helping method.