Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine最新文献

Torsional performance is a critical evaluation criterion in the design of peripheral vascular stents, enabling them to adapt to the deformation of the vessel to reduce damage to the vascular wall and thus avoiding in-stent restenosis (ISR). Therefore, this study employed the finite element method (FEM) to investigate the impact of stent design parameters on the torsional behavior of self-expanding peripheral vascular stents. These parameters included stent diameter and thickness, as well as the length and width of struts and links. Results revealed that among all parameters, strut length and width significantly influence the stent torsional performance, whereas link width has a lesser effect. Notably, increasing strut length and decreasing strut width were found to significantly reduce the required torque, with the twist metric (TM) reduced by approximately 86.3% when strut length increased from 1.2 to 2.8 mm. Moreover, reductions in stent diameter and thickness, alongside an increase in link length, further contributed to a decrease in TM, thereby enhancing the stent torsional performance. This study may provide insights for better peripheral stent design and clinical decision of stent choice.

The Trunk Impairment Scale Version 2.0 (TIS 2.0) measures the motor impairment of the trunk after a stroke through the evaluation of dynamic sitting balance and co-ordination of trunk movement. Evaluations by physiotherapists depend on their ability in detecting minor changes in motion and observing limb movements and these can be time consuming and reduce their availability for rehabilitation work. An automated scoring system for TIS 2.0 was proposed to provide a more reproducible and standardized alternative to manual physiotherapist assessments. In the development phase, motion data from lay actors simulating stroke condition were collected using video motion capture system OpenCap. This data was utilized to create metrics and establish cut-off values for a rule-based classification. The discriminant abilities of the metrics were evaluated using the area under the curve (AUC). In the testing phase, the performance of the developed system was assessed on 19 stroke survivors (Berg Balance Scale score of 20-55) using both automated system and manual scoring by nine physiotherapists. The discriminant abilities of the features used in the dynamic sitting balance subscale are considered excellent to outstanding (AUC ≥ 0.717), and coordination subscale ranged from poor to outstanding (AUC ≥ 0.667). The automated scores aligned with physiotherapists' scores, achieving an average percentage of agreement 71.1%. The total TIS 2.0 scores generated by the automated method showed moderate correlation with the sum of mode-determined task scores (R = 0.526, p < 0.05). These findings suggest that the proposed automated system demonstrates comparable validity to assessments by physiotherapists.

Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The objective of the current study is to introduce a data augmentation strategy that relies on a variational autoencoder to generate synthetic data of kinetic and kinematic variables. The kinematic and kinetic variables consist of hip and knee joint angles and moments, respectively, in both sagittal and frontal plane, and ground reaction forces. Statistical parametric mapping (SPM) did not detect significant differences between real and synthetic data for each of the biomechanical variables considered. To further evaluate the effectiveness of this approach, a long-short term model (LSTM) was trained both only on real data (R) and on the combination of real and synthetic data (R&S); the performance of each of these two trained models was then assessed on real test data unseen during training. The principal findings included achieving comparable results in terms of nRMSE when predicting knee joint moments in the frontal (R&S: 9.86% vs R: 10.72%) and sagittal plane (R&S: 9.21% vs R: 9.75%), and hip joint moments in the frontal (R&S: 16.93% vs R: 16.79%) and sagittal plane (R&S: 13.29% vs R: 14.60%). The main novelty of this study lies in introducing an effective data augmentation approach in motion analysis settings.

Assessing the kinematics of the upper limbs is crucial for rehabilitation treatment, especially for stroke survivors. Nowadays, researchers use computer vision-based algorithms for Human motion analysis. However, specific challenges include less accuracy, increased computational complexity and a limited number of anatomical key points. This study aims to develop a novel algorithm using the MediaPipe framework to estimate five specific upper limb movements in stroke survivors. A single mobile camera recorded the movements on their affected side in a study involving 10 hemiplegic patients. The algorithm was then utilized to calculate the angles associated with each movement, and its accuracy was validated against standard goniometer readings, showing a mean bias within an acceptable range. Additionally, a Bland-Altman analysis demonstrated a 95% limit of agreement between the algorithm's results and those of the Goniometer, indicating reliable performance. The MediaPipe framework provides several advantages over other methods like OpenPose and PoseNet, such as several anatomical key points, improved precision and reduced execution time. This algorithm facilitates efficient measurement of upper limb movement angles in stroke survivors and allows for straightforward tracking of mobility improvements. Such innovative technology is a valuable tool for healthcare professionals assessing upper limb kinematics in rehabilitation settings.

The use of ultrasound contrast agents (UCAs) for estimating portal pressure has recently gained attention due to its clinical promise, yet variability in acoustic amplitude poses challenges. UCAs contain microbubbles (1-10 µm in diameter), and understanding their acoustic response is essential to address this variability. However, systematic exploration of factors influencing microbubble behavior remains limited in current literature. This paper introduces a novel finite element analysis-based framework for portal pressure estimation, bridging key gaps. Developed in two stages, the model first captures the subharmonic response of a single bubble to an acoustic excitation of 50 kPa at 4 MHz, highlighting the influence of bubble size on resonance frequency. In the second stage, single-bubble responses are extended to analyze how microbubble population, size, and spatial distribution affect portal pressure estimation. For the first time, this study elucidates the experimental scatter in pressure measurements through a comprehensive consideration of these variables, offering new directions for UCA-based clinical pressure estimation in applications such as portal and cardiac pressure assessment.

Bone is a highly heterogeneous and anisotropic material with a hierarchical structure. The effect of diaphysis locations and directions of loading on elastic-plastic compressive properties of bovine femoral cortical bone was examined in this study. The impact of location and loading directions on elastic-plastic compressive properties of cortical bone was found to be statistically insignificant in this study. The variances of most of the compressive properties were also observed to be location and directionality independent except for the locational differences in modulus of resilience (distal to central for longitudinal loading) and plastic work (central to distal for transverse loading) as well as differences in variances of the modulus of resilience and elastic modulus values for two directions of loading. The micro-mechanisms of cortical bone failure for longitudinal and transverse directions of loading were considered to be responsible for the difference in variances in the later properties values as well as for the maximum and minimum coefficient of variation (CV) obtained for compressive properties in two directions of loading. The representative cubical volume at the tested hierarchical level contained all unique microstructural features of the plexiform bone and therefore produced the homogeneous and isotropic elastic-plastic compressive properties of cortical bone. It is expected that the outcome of this study may be helpful in the area of bone tissue engineering and finite element simulation of cortical bone.

Subject-specific finite element models of knee joint contact mechanics are used in assessment of interventions and disease states. Cartilage thickness distribution is one factor influencing the distribution of pressure. Precision of cartilage geometry capture varies between imaging protocols. This work evaluated the cartilage thickness distribution precision needed for contact mechanics prediction in models of the tibiofemoral joint by comparing model outputs to experimental measurements for three cadaveric specimens. Models with location-specific cartilage thickness were compared to those with a uniform thickness, for a fixed relative orientation of the femur and tibia and with tibial freedom of movement. Under constrained conditions, the advantage of including location-specific cartilage thickness was clear. Models with location-specific thickness predicted the proportion of force through each condyle with an average error of 5% (compared to 27% with uniform thickness) and predicted the experimental contact area with an error of 21 mm² (compared to 98 mm² with uniform thickness). With tibial freedom, the advantage of location-specific cartilage thickness not clear. The attempt to allow three degrees of relative freedom at the tibiofemoral joint resulted in a high degree of experimental and computational uncertainty. It is therefore recommended that researchers avoid this level of freedom. This work provides some evidence that highly constrained conditions make tibiofemoral contact mechanics predictions more sensitive to cartilage thickness and should perhaps be avoided in studies where the means to generate subject-specific cartilage thickness are not available.

This paper creates 3D models of Kitchon Root Controlled Auxiliary Archwire (Kitchon-RCAA) with different material properties and assembles them onto the main archwire equipped with brackets. By setting different loading methods and conducting Finite Element Analysis (FEA), the range of Orthodontic Torque/Support Force (OT/SF) values can be obtained. From the obtained values, it can be seen that changes in material properties have a significant impact on the mechanical properties of Kitchon-RCAA. When the properties of the Kitchon-RCAA material change two or more times, the mechanical values generated by Kitchon-RCAA cannot be directly added from two or more separate changes in the properties of the material. Therefore, it is necessary to simulate the model after each parameter change to obtain new results. And then the maxillary bio-model is reconstructed in reverse based on Cone Beam Computerized Tomography (CBCT) images. The biomechanical data equivalent to the mechanical mechanics generated by the root control assisted archwire is also added to the corresponding tooth positions, making indirect orthodontic behavior of Kitchon-RCAA on teeth possible. From the obtained results, it can be seen that the von Mises stress and total deformation magnitude for both normal teeth and corresponding Periodontal Ligament (PDL) position show a stable trend, while the Right Cuspid (R-C) and corresponding PDL with malformed root have a large stress concentration and may have a mold penetration problem. Overall, this paper not only analyses the mechanical behavior of the Kitchon-RCAA, this article not only analyzed the mechanical behavior of Kitchon-RCAA, but also its effect on the indirect biomechanical behavior of the teeth and PDL. And in combination with simulation result nephograms, it also enables predictability and visualization of orthodontic results. This helps dentists to provide safer and more reliable individualized orthodontic treatment plans for patients.

High-efficiency and high-quality sterilization technologies for medical materials can significantly reduce iatrogenic infection. This study investigates the synergistic effects of laser-induced periodic surface structures (LIPSS) and ultrasonic cleaning on the removal of bacteria from medical material surfaces. We specifically examined how ultrasonic parameters and structural defects in LIPSS impact the effectiveness of bacterial removal. As an emerging medical metal, Zr-BMG was chosen for the target material. Femtosecond laser processing was employed to create LIPSS with both complete linear arrays and discontinuous linear arrays structures featuring surface defects by adjusting the scanning overlap rate. A high-concentration solution of S. aureus was used for co-cultivation, resulting in a surface bacterial coverage rate exceeding 95%. The study analyzed the synergistic sterilization effect of microstructured surfaces through variations in ultrasonic cleaning power and duration. The results indicated that surfaces with microstructures demonstrated significantly improved bacterial removal following ultrasonic cleaning. The bacterial removal rate was found to be proportional to the ultrasonic vibrator power, and the surface with a LIPSS structure outperformed the discontinuous LIPSS surface in bacterial removal efficiency. Optimal results were achieved with the LIPSS surface after 30 min of cleaning at 100 W ultrasonic power. However, there was minimal difference in bacterial removal between 10 and 30 min at the same power level. This study aims to provide methodological insights and data support for the efficient and high-quality cleaning of medical metal surfaces.

Human motion has been analyzed for decades based on experimentally collected subject data, serving various purposes, from enhancing athletic performance to assisting patients' recovery in rehabilitation and many individuals can benefit significantly from study advancements. Human motion prediction, is a more challenging task because no experimental data are available in advance, particularly concerning repetitive tasks, such as box lifting and tossing, to prevent injury risks. Tossing, a common task in various industries, involves the simultaneous vertical and horizontal movement of objects but often results in bodily strain. This paper presents an optimization-based method for predicting two-dimensional (2D) symmetric tossing motion without relying on experimental data. The method employs sequential quadratic programming, which optimizes dynamic effort by incorporating both static and dynamic joint torque limits. To validate the proposed model, experimental data were collected from 10 subjects performing tossing tasks using a motion capture system and force plates. The predicted joint angles and ground reaction forces considering dynamic joint strength constraints were compared with their corresponding experimental data to validate the model. In addition, the predicted joint torques differences are compared between joint dynamics strengths and static strengths. The results showed that the predicted optimal tossing motions range between the maximum and minimum of the experimental standard deviation for kinematic data across all subjects and the ground reaction forces are also within the experimental data range. This supports the validity of the prediction model. The findings of this study could have practical applications, especially in preventing the potential risk of injuries among workers who have daily tossing jobs.