Computer Methods in Biomechanics and Biomedical Engineering最新文献

Ice hockey is a sport with a high incidence of concussion, but growing attention has been placed on the broader exposure to head impacts, regardless of clinical symptoms, due to their potential cumulative effects. While player education, protective equipment, and rule modifications have been introduced to reduce head impact risk, the effect of rule changes on head impact exposure in adult professional ice hockey has not been well quantified. This study compared the frequency and magnitude of head impacts between two NHL seasons of 2003-04 and 2016-17, that bracketed a period of rule changes targeting contact to the head. Twenty regular-season games from each season were analyzed using a combination of video analysis, physical reconstructions, and finite element modeling to estimate brain tissue strain for each head impact. Total head impact frequency per game did not differ significantly between seasons. However, a significant decrease in glove-to-head contacts and fight-related impacts was observed in 2016-17, reflecting the enforcement of stricter penalties for these actions. Additionally, players in the 2016-17 season experienced a significantly higher frequency of impacts classified within the Low brain strain category. These results suggest that while targeted rules may reduce specific types of dangerous contact, they may also shift the nature of impacts without reducing overall exposure. Understanding how such shifts influence cumulative biomechanical load is essential for guiding future rule evaluations and injury prevention strategies in professional ice hockey.

Prosthetic heart valve (PHV) replacement is an effective treatment for valvular heart disease. The mechanical behavior of heart valves, encompassing solid mechanics, fluid dynamics, and FSI, is fundamental to diagnosing dysfunction and guiding design. This review critically analyzes PHV mechanics, including constitutive modeling, stiffness and strength evaluation, vibroacoustic response, fatigue and fracture, FSI simulation, viscoelastic effects, and hemodynamics. It further summarizes numerical, experimental, and AI-assisted investigation methods, and outlines current challenges and future directions in computational mechanics for PHVs, providing interdisciplinary insights for bioengineering and biomedical applications.

We sought to establish whether dynamic Bayesian optimization (DBO) is a suitable algorithm for human-in-the-loop-optimization (HILO) of the control input of devices interacting with individuals whose output changes during optimization as resulting from motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming responses from state-space models of motor learning. DBO generally outperformed standard Bayesian optimization (BO) in convergence to optimal inputs and outputs after a certain number of iterations. DBO may improve the performance of HILO over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability and learning.

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients' subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.

This study uses transfer learning architectures to detect cardiac murmurs in phonocardiogram signals by denoising the signal, extracting relevant features for spectrograms generation. The Short-Time Fourier Transform, Mel-Frequency Cepstral Coefficients, and Continuous Wavelet Transform techniques were applied on Physionet's CirCor Digiscope PCG dataset. VGG16, VGG19, ResNet50, and InceptionV3 models, were trained on these spectrograms for binary classification. Fourth-order Butterworth bandpass filter, used with Savitzky-Golay filtering, gave the best results. The CWT Spectrogram and VGG19 combination yielded best accuracy of 89.44%. Different combinations of spectrograms and transfer learning architectures performed better on performance metrics of precision, recall, F1-score, and ROC-AUC.

In this study, we established a system of differential equations with piecewise constant arguments to explain the impact of epidemiological transmission between different locations. Our main goal is to look into the need for vaccines as well as the necessity of the lockdown period. We proved that keeping social distance was necessary during the pandemic spread to stop transmissions between different locations and that re-vaccinations, including screening tests, were crucial to avoid reinfections. Using the Routh-Hurwitz Criterion, we examined the model's local stability and demonstrated that the system could experience Stationary and Neimark-Sacker bifurcations depending on certain circumstances.

A validated femoral neck fracture model stabilized with three inverted cannulated screws was used to consider different intraoperative scenarios when the inferior screw hole is inadvertently started too inferiorly. These scenarios were to: (1) abandon the misplaced inferior screw hole and restart this hole more proximally, or (2) accept the mispositioned placement of the inferior screw and insert the remaining superior screws parallel or convergent to the inferior screw. Utilizing the second option and accepting the errant hole was associated with the greatest interfragmentary motion and stresses in the bone and hardware. In contrast, the first option created an improved mechanical environment for healing.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

In a vascular interventional surgery robot(VISR), a high transparency master-slave system can aid physicians in the more precise manipulation of guidewires for navigation and operation within blood vessels. However, deformations arising from the movement of the guidewire can affect the accuracy of the registration, thus reducing the transparency of the master-slave system. In this study, the degree of the guidewire's deformation is analyzed based on the Kirchhoff model. An unsupervised learning-based guidewire shape registration method(UL-GSR) is proposed to estimate geometric transformations by learning displacement field functions. It can effectively achieve precise registration of flexible bodies. This method not only demonstrates high registration accuracy but also performs robustly under different complexity degrees of guidewire shapes. The experiments have demonstrated that the UL-GSR method significantly improves the accuracy of shape point set registration between the master and slave sides, thus enhancing the transparency and operational reliability of the VISR system.

The clinical performance of biodegradable polymer stents implanted in blood vessels is affected by uneven degradation. Stress distribution plays an important role in polymer degradation, and local stress concentration leads to the premature fracture of stents. Numerical simulations combined with in vitro experimental validation can accurately describe the degradation process and perform structural optimization. Compared with traditional design techniques, optimization based on surrogate models is more scientifically effective. Three stent structures were designed and optimized, with the effective working time during degradation as the optimization goal. The finite element method was employed to simulate the degradation process of the stent. Surrogate models were employed to establish the functional relationship between the design parameters and the degradation performance. The proposed function models accurately predicted the degradation performance of various stents. The optimized stent structures demonstrated improved degradation performance, with the kriging model showing a better optimization effect. This study provided a novel approach for optimizing the structural design of biodegradable polymer stents to enhance degradation performance.