Computer Methods in Biomechanics and Biomedical Engineering最新文献

Metatarsal stress fractures (MSF), particularly the 2^nd and 3^rd MSF, are common injuries among athletes. Although there are several practices to reduce foot and ankle injuries, there is no injury prevention program specifically designed to minimize MSF. This is mainly due to the lack of information about the loadings/postures that cause MSF. Therefore, this study aimed to investigate dangerous loadings/postures potentially causing MSF during push-off (PO). The analysis was conducted with Finite Element Modelling (FEM), calibrated with the three-point bending test, and validated with peak plantar pressure (PPP) and fracture force measurement. Extended Finite Element Method was used for MSF simulation such that ten different foot and ankle configurations were designed, with five for each of the 2^nd and 3^rd MSF under pure vertical loadings. A more complex loading, ankle eversion/inversion during PO, was also examined for the MSF. The average error percentage for the calibration of the model with the three-point bending test was 3.05%. The average error percentages for the validation of the model with PPP and fracture force measurements were 18% and 30%, respectively. The outcomes of pure vertical loadings indicated the higher potential for the 2^nd and 3^rd MSF at 30% PO and 70% PO, respectively. The results of ankle eversion/inversion loadings represented that the most dangerous posture for MSF was 30° ankle eversion for the 3^rd metatarsal at 70% PO. These results provide a guide, including what postures to avoid for the 2^nd and 3^rd MSF among people who are at high risk of MSF.

The improvement in congenital heart disease (CHD) treatment and management has increased the life expectancy in infants. However, the long-term efficacy is difficult to assess and thus, computational modelling has been applied for evaluating this. Here, we provide an overview of the applications of computational modelling in CHD based on three categories; CHD involving large blood vessels only, heart chambers only, and CHD that occurs at multiple heart structures. We highlight the advancement of computational simulation of CHD that uses multiscale and multiphysics modelling to ensure a complete representation of the heart and circulation. We provide a brief future direction of computational modelling of CHD such as to include growth and remodelling, detailed conduction system, and occurrence of myocardial infarction. We also proposed validation technique using advanced three-dimensional (3D) printing and particle image velocimetry (PIV) technologies to improve the model accuracy.

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young's modulus ($E$) and Poisson's ratio ($\nu$) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Hematogenous metastasis occurs when cancer cells detach from the extracellular matrix in the primary tumor into the bloodstream or lymphatic system. Elucidating the response of metastatic tumor cells in suspension to the flow conditions in lymphatics with valves from a mechanical/fluidic perspective is necessary. A physiologically relevant computational model of a lymphatic vessel with valves was constructed using fully coupled fluid-cell-vessel interactions to investigate the effects of lymphatic vessel contractility, valve properties, and cell size and stiffness on the variations in magnitude and gradient of the flow-induced wall shear stress (WSS) experienced by suspended tumor cells. Results indicated that the maximum WSS_max increased with the increments in cell diameter, vessel contraction amplitude, and valve stiffness. The decrease in vessel contraction period and valve aspect ratio also increased the maximum WSS_max. The influence of the properties of the valve on the WSS was more significant among the factors mentioned above. The maximum WSS_max acting on the cancer cell when the cell reversed the direction of its motion in the valve region increased by 0.5-1.4 times that before the cell entered the valve region. The maximum change in WSS was in the range of 0.004-0.028 Pa/µm depending on the factors studied. They slightly exceeded the values associated with breast cancer cell apoptosis. The results of this study provide biofluid mechanics-based support for mechanobiological research on the metastasis of metastatic cancer cells in suspension within the lymphatics.

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method's effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the "STEW" dataset, which consists of two tasks, namely "No task" and "simultaneous capacity (SIMKAP)-based multitasking activity". We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

OpenSim Moco enables solving for an optimal motion using Predictive and Tracking simulations. However, Predictive simulations are computationally prohibitive, and the efficacy of Tracking in deviating from its reference is unclear. This study compares Tracking and Predictive approaches applied to the generation of morphology-specific motion in statistically-derived musculoskeletal shoulder models. The signal analysis software, CORA, determined mean correlation ratings between Tracking and Predictive solutions of 0.91 ± 0.06 and 0.91 ± 0.07 for lateral and forward-reaching tasks. Additionally, Tracking provided computational speed-up of 6-8 times. Therefore, Tracking is an efficient approach that yields results equivalent to Predictive, facilitating future large-scale modelling studies.

This study aimed to elucidate the vertebral bone density variations associated with adolescent idiopathic scoliosis (AIS), specifically examining the impact of unilateral muscle paralysis using an integrated approach combining Frost's Mechanostat theory, a three-dimensional subject-specific finite element model and a musculoskeletal model of the L2 vertebra. The findings revealed a spectrum of bone density values ranging from 0.29 to 0.31 g/cm3, along with vertebral micro-strain levels spanning from 300 to 2200, consistent with existing literature. Furthermore, the ratio of maximum von Mises stress between the concave and convex side in the AIS model with intact muscles was approximately 1.08, which decreased by 4% due following unilateral paralysis of longissimus thoracis pars thoracic muscle. Overall, this investigation contributes to a deeper understanding of AIS biomechanics and lays the groundwork for future research endeavors aimed at optimizing clinical management approaches for individuals with this condition.

Although the safety performance of guardrail end terminals is tested using crash tests in the U.S., occupant injury risks are evaluated based on the flail-space model. This approach developed in the early 1980s neglects the influence of safety features (e.g. seatbelt, airbags, etc.) installed in late model vehicles. In this study, a vehicle (sedan, 1100 kg), a guardrail end terminal (ET-Plus) and a human body model (Global Human Body Model Consortium, GHBMC) were integrated to simulate car-to-end terminal crashes. Five velocities, two offsets, and two angles were used as pre-impact conditions. In all the 20 simulations, kinematics and kinetic data were recorded in GHBMC and vehicle models to calculate the GHBMC injury probabilities and vehicle-based injury metrics, correspondingly. Pre-impact velocity was observed to have the largest effect on the occupant injury measures. All the body-region and full-body injury risks increased with the increasing velocity. Meanwhile, the angles had a larger effect than offset to the change of full-body injury risk (9.1% vs. 0.3%). All the vehicle-based metrics had good correlations to full-body injury probabilities. Occupant Impact Velocity (OIVx), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) had a good correlation to chest, thigh, upper tibia, and lower tibia injuries. All the other correlations (e.g. brain/head injuries) were not statistically significant. The results pointed out that more vehicle-based metrics (ASI and THIV) could help improve the predictability in terms of occupant injury risks in the tests. Numerical methodology could be used to assess head and brain injury probabilities, which were not predictable by any vehicle-based metrics.

In this work, we investigate the effects of stent indentation on hemodynamic indicators in stented coronary arteries. Our aim is to assess in-silico risk factors for in-stent restenosis (ISR) and thrombosis after stent implantation. The proposed model is applied to an idealized artery with Xience V stent for four indentation percentages and three mesh refinements. We analyze the patterns of hemodynamic indicators arising from different stent indentations and propose an analysis of time-averaged WSS (TAWSS), topological shear variation index (TSVI), oscillatory shear index (OSI), and relative residence time (RRT). We observe that higher indentations display higher frequency of critically low TAWSS, high TSVI, and non-physiological OSI and RRT. Furthermore, an appropriate mesh refinement is needed for accurate representation of hemodynamics in the stent vicinity. The results suggest that disturbed hemodynamics could play a role in the correlation between high indentation and ISR.