Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study investigates the detection of auditory steady-state responses for automatic tonal audiometry using sequential test strategies. Monte Carlo simulations are employed to optimize strategy parameters, aiming to minimize mean examination time while maintaining detection power. Optimization criteria are based on the areas under probability of detection and examination time versus signal-to-noise ratio curves. The optimal parameters obtained from simulations are compared with results from real EEG data. The simulated optimal settings closely matched those derived from real data, except for small epoch sizes, demonstrating the effectiveness of the proposed simulation-based optimization approach.

This study aims to clarify the relationship between pedestrian leg bone fracture, the head, chest, abdomen and pelvis kinematic responses, and knee joint injury severity in car-pedestrian collisions, based on a coupled FE model with two scenarios: leg bone fracture and non-fracture. It finds leg fractures cause abdominal and pelvic upward Z-axis shifts, reduce knee ligament elongation rates and reduce femur/tibia peak bending moment, but have no significant effects on head and chest kinematics and injuries. This study demonstrates that kinematic responses of abdomen and pelvis and knee injury severity are significantly influenced by leg fracture.

Motor imagery (MI) EEG classification, a core BCI task, faces challenges due to EEG's low signal-to-noise ratio and non-stationarity. Traditional supervised learning methods perform poorly in cross-subject and small-sample scenarios, limiting practical use. We propose CMHA-Net, a MI-EEG-optimized CNN integrating depthwise separable convolution, deep convolution and multi-head attention, combined with a Meta-SGD-based meta-transfer learning framework. Experiments on BCI-IV-2a and High Gamma datasets show 81.61% and 88.15% accuracy, outperforming existing models by 4-15% and excelling in small-sample cases, advancing clinical and real-world BCI applications.

This study refines the SIDARTHE model for Italy's COVID-19 outbreak using a hybrid, data-driven framework. A two-stage approach compares Maximum Likelihood Estimation (MLE) with Physics-Informed Neural Networks (PINNs) for parameter estimation, then applies Symbolic Regression (via gplearn and PySR) to optimize the governing equations. Results show PINNs surpass MLE in accuracy, and PySR outperforms gplearn in deriving robust expressions. The final integrated model-combining PINN estimation with Symbolic Regression-significantly reduces predictive uncertainty and aligns closely with observed data, providing a resilient tool for public health planning.

With the advancement of modern medicine and engineering, breast deformation analysis has become increasingly important in the fields such as cosmetic surgery, reconstructive surgery, sports biomechanics, and health-related research. The morphological changes in breast tissue not only affect an individual's aesthetic appearance and mental well-being but are also closely linked to breast health. Finite Element Method (FEM) and Machine Learning (ML) are two advanced technologies that exhibit significant potential in breast deformation analysis. FEM plays a crucial role in modeling the mechanical behavior of breast soft tissues and analyzing deformations due to its precise numerical solutions and realistic simulation capabilities. On the other hand, machine learning techniques provide new perspectives for personalized health management and disease risk assessment by processing large-scale breast morphology data to uncover patterns and correlations. This paper reviews the latest advancements in the application of FEM and ML in breast tissue morphology analysis, explores their potential and challenges in simulating both static and dynamic breast deformations in clinical practice, and summarizes the characteristics and application scenarios of both technologies. This paper discusses the novel opportunities brought by the integration of these two approaches for the clinical diagnosis and analysis of breast-related diseases. It synthesizes these developments and explores the potential benefits of methodological integration for future breast morphology-related research and clinical applications.

Peristaltic pumps are promising for wearable dialysis, but optimizing flow and hemocompatibility under roller-tube contact is limited by costly 3D two-way fluid-structure interaction (FSI). We propose a surrogate-assisted three-objective workflow that couples 3D two-way FSI, a PSO-BPNN surrogate, NSGA-III search, and entropy-weighted TOPSIS decision-making. Sixty samples were used to train the surrogate for average flow rate $f_{\text{ave}}$, time-averaged shear stress ${\tau }_{\text{ave}}$, and shear-stress pulsation ${\tau }_p$, achieving $R^2$ values of 0.97/0.91/0.94. Compared with the baseline pump, the selected design delivers 208.79 mL/min, reduces ${\tau }_{\text{ave}}$ and ${\tau }_p$ by 6.30% and 40.76%, lowers the predicted hemolysis index by 6.71%.

People with spinal cord injury (SCI) show impaired thermoregulation during exercise, making skin temperature a noninvasive indicator. This study applies hybrid Extreme Learning Machine (ELM) models optimized with Ant Lion, Dragonfly, and Evolution Strategy algorithms to predict skin and core temperature dynamics during graded arm-crank exercise in 32 participants (16 SCI, 16 controls). The Dragonfly-optimized ELM achieved the highest accuracy (R² = 99.705, RMSE = 0.014) with no significant difference between predicted and measured core temperatures (p > 0.05). Feature-importance and SHAP analyses identified peak power output, group, and stage as dominant predictors, indicating reduced peripheral thermoregulatory variability in SCI.

This study developed a computational framework for mechano-biological regulation of fracture healing, integrating four finite element modules: mechanical stimulus calculation, vascular reconstruction prediction, cell migration and differentiation, and tissue modulus updating. Based on this framework, the effects of initial geometric conditions, initial modulus of granulation tissue, mesenchymal stem cell (MSC) diffusion coefficient, vascular reconstruction parameters, and loading conditions on the speed and quality of healing were systematically analyzed. The results demonstrate that initial conditions play a significant regulatory role in the rate and quality of fracture healing.

Plate and screw osteosynthesis remains essential in orthopedics especially for the fixation of femoral fracture and other long bone injury. The objective of this investigation was to analyze the behavior of a titanium-plated and screwed femur during realistic loading while analyzing stress, strain, and displacement. The square, round, trapezoidal, and triangular screw thread profiles were investigated in terms of their transmission behavior on the head, shaft, and plates. Square thread profiles distribute the stress more uniformly, which results in a reduction in both peak and global von Mises values and the potential for microfracture, loosening, and failure. The triangular and trapezoidal threads initiated the development of local stress hotspots at the plate eyelets, as well as at screw bone interface. The twisted threads demonstrated a moderate overall favorable response. Plate stress patterns were material-property dependent, and strain and deformation increased linearly with the load. Square and round threads increased construct stiffness. In general, screw geometry and plate choice have been identified as major levers for design-informed personalized osteosynthesis.

Atherosclerosis in the carotid arteries increases stroke risk, yet current treatment decisions rely mainly on stenosis degree, which poorly reflects individual vulnerability. We present an ultrasound-based computational fluid dynamics (CFD) framework for patient-specific hemodynamic assessment. Using tracked 2D ultrasound and automated segmentation, we reconstructed carotid geometries for five healthy subjects and three patients with severe stenoses. CFD simulations quantified TAWSS, OSI, RRT, and helicity, visualized through risk maps. Healthy arteries showed localized risk near bifurcations, whereas stenosed geometries exhibited extensive disturbed flow and altered helicity patterns. This approach demonstrates the feasibility of ultrasound-driven CFD for personalized risk mapping and highlights helicity's potential as a diagnostic marker.