Medical Engineering & Physics最新文献

The use of bio-adhesives in sternal fixation aims to mitigate complications commonly associated with median sternotomy, which can lead to significant morbidity and mortality rates. Bio-adhesives are recognized for enhancing sternal fixation and limiting hemisterna displacement. This study evaluates the effectiveness of glass polyalkenoate cements (GPCs) derived from a novel BT101 glass in conjunction with a new spot weld application technique. Finite element analysis (FEA) was used to predict the minimum GPC adhesive coverage necessary to prevent pathological displacement of the hemisterna. Three sternal fixation models with varying GPC adhesive coverage 50%, 62.5%, and 75% were developed in SolidWorks and analyzed in Ansys software. The simulations applied a breathing load of 500 N and a wiring clamping force of 1000 N to replicate experimental conditions. The FEA results demonstrated a 21.4% reduction in directional displacement of the sternum with full adhesive coverage compared to traditional wire-only fixation. The maximum directional deformation for 50%, 62.5%, 75%, and 100% of adhesive coverage are 1.576 ± 0.232 mm, 1.281 ± 0.182, 0.999 ± 0.0262, and 0.29 ± 0.28, respectively, all of which are below the pathological displacement threshold of 2.0 mm. The findings indicate that increased adhesive coverage correlates with reduced sternal displacement. Consequently, the study recommends using wired sternal fixation enhanced with 75% GPC spot welds to minimize hemisterna displacement, potentially enhancing ossification and bone healing, and improve vascularization between the sternal halves at the spaces between adhesive spots. Thus, the development of the sternal fixation finite element model could be useful in parallel with the experimental analysis.

Dysplasia is a recognized risk factor for osteoarthritis (OA) of the hip, early diagnosis of dysplasia is important to provide opportunities for surgical interventions aimed at reducing the risk of hip OA. We have developed a pipeline for semi-automated classification of dysplasia using 3D surface models obtained from volumetric CT scans of patients' hips and a minimal set of four clinically annotated landmarks on the acetabular rim (the most proximal, distal, anterior and posterior aspects), combining the framework of the Gaussian process latent variable model with diffeomorphism to create a statistical shape model (SSM), which we termed the Gaussian process diffeomorphic SSM (GPDSSM). We used 192 CT scans, 100 for model training and 92 for testing. The GPDSSM effectively distinguishes dysplastic samples from controls while also highlighting regions of the underlying surface that show dysplastic variations. As well as improving classification accuracy compared to angle-based methods (AUC 96.2% vs 91.2%), the GPDSSM can save time for clinicians by removing the need to manually measure angles and interpreting 2D scans for possible markers of dysplasia.

To address the prevailing challenges associated with the screening of knee osteoarthritis (KOA), which include the high costs associated with imaging technologies, intricate procedural requirements, and the lack of dynamic functional information, this study introduces a multimodal gait analysis approach utilizing wearable inertial measurement units. This approach involves the conversion of time-series gait data into corresponding Gramian Angular Field (GAF) images. A dual-channel architecture was developed, integrating temporal convolutional networks (TCNs) and depth-wise separable convolutional neural networks, with multimodal feature fusion facilitated by a multi-head attention (MHA) mechanism. The experimental results demonstrated that the proposed model achieved an accuracy of 97.87%, a precision of 98.23%, a recall of 98.17%, and an F1-score of 98.19% in ten-fold cross-validation on our dataset, outperforming various established time-series models and single-modal approaches. This study substantiates that integration of GAF images within a multimodal framework significantly improves screening sensitivity and robustness, with the characteristics of high accuracy, cost-effectiveness, and radiation-free operation.

This paper presents a novel diagnosis approach to sensor-based acoustic emission (AE) for the assessment of the dynamic integrity of the human knee joint, along with its efficacy on healthy and osteoarthritis (OA) subjects. A total of 121 humans with increasing ages from different healthy to OA knee conditions participated in this study. AE hits and other signal parameters, including signal amplitude (measured in decibels), rise time, duration, absolute energy, and signal strength, are analyzed in conjunction with joint angles during sit-to-stand (S-T-S) activity. The analysis is performed across four distinct movement phases to assess variations in knee joint conditions. In healthy subjects, bilateral symmetry in acoustic hits is observed, indicating comparable AE activity in both legs. Acoustic hits specifically refer to the total number of detected AE events during joint movement, providing a key quantitative measure for evaluating OA changes and overall knee joint health. Acoustic hits and signal amplitude showed a significant increase in OA subjects compared to healthy individuals. The statistical evaluation of time and energy signal features revealed a significant difference between healthy and osteoarthritis groups (p < 0.001 at 95% confidence interval (CI) for healthy group 3 andp < 0.001 at 95% CI for the OA group). In OA groups, the signal duration is four times longer (p < 0.001 at 95% CI), and absolute energy is 26 times higher (p < 0.001 at 95% CI) than in healthy subjects (group 3). Here, the statedp-values are obtained from thet-test.Cumulative probability index analysis established a linear and non-linear trend among the groups, and OA subjects are identified with the most deviated feature patterns in AE detection. From the study outcomes, a strong basis is formed for the development of sensor-based wearable systems in the early diagnosis of OA.

Aortic mechanical heart valves (MHVs) have been implanted for decades to treat aortic valve disease and remain a viable option when valve durability is prioritized. However, the non-physiological hemodynamics induced by MHVs may lead to adverse clinical outcomes. Fluid-structure interaction (FSI) simulations enable the analysis of the biomechanical interaction between MHVs and blood flow. This study presents a strongly coupled, boundary-fitted FSI framework for aortic MHVs, used to assess the impact of MHV design and aortic curvature on hemodynamics. Nine simulation scenarios were investigated, considering three commercially available MHVs and three idealized aortic geometries (one straight and two curved models). Overall, the framework proved to provide results for flow-rate waveforms, velocity fields, and leaflet kinematics aligning well with previous experimental and computational studies. The framework highlighted that: (i) MHV design influences velocity fields and large-scale vorticity transport in the aorta, with systolic differences among the three devices of up to 41% and 133% in average swirling strength and stretching, respectively; (ii) the straight aortic model underestimates systolic swirling strength (up to 56%) and stretching (up to 91%) compared to curved models. This FSI framework can support MHV development by analyzing different device designs and anatomical scenarios.

Lumbar spinal disorder is a chief cause of disability leading to medical intervention, specifically for elderly people worldwide. The present study aims to investigate and evaluate the biomechanical performance of the expandable pedicle screw vis-à-vis normal pedicle screw with a focus on the load transfer and bone remodelling around the lumbar vertebra. A strain energy density (SED)-based bone remodelling algorithm in combination with three-dimensional finite element (FE) models of intact and implanted lumbar spine FSU was used to investigate the changes in bone density around the screws. The loading (flexion, extension, torsion, lateral bending) and boundary conditions influenced strain, and SED distribution within the FSU. In case of normal pedicle screws, bone apposition was primarily concentrated near the screw insertion region in both L4 (60%-80%) and L5 (30%-90%) vertebra. Bone resorption was estimated to be in the posterior region, near screw length in L4 (10%-45%) and in central anterior right side and posteriorly in L5 vertebra (20%-60%). For expandable pedicle screws, bone apposition was predicted in a relatively larger area in central anterior screw insertion region for L4 vertebra (50%-75%) and L5 vertebra (40%-75%). Bone resorption was found in the central anterior right side as well as on posterior side of L4-L5 vertebra although the area was comparatively less. Bone apposition was significantly greater near the cage insertion area for both pedicle types. It was estimated that the average bone density increase was greater in case of expandable pedicle screws than that for normal pedicle screws. Also, the average density in L5 vertebra was higher than the L4 vertebra. The overall result appears to favour expandable pedicles over normal ones so far as bone remodelling is concerned.

The utilization of a plate-screw system is a prevalent osteosynthesis technique widely employed for the fixation of mandibular fractures. While comparable findings exist regarding the impact of using locking and non-locking screws on the mandibular bone, conflicting results have been reported concerning their effect on stress distribution in the plate and screws. This study investigates the influence of locking and non-locking plate-screw systems on mandibular fracture fixation using finite element analysis, incorporating a 3D scanned jawbone geometry. Results indicate that locking screws exhibit a more uniform stress distribution while experiencing higher stress values compared to non-locking screws. Furthermore, the maximum stress in non-locking plates is lower than that in locking plates. In fact, replacing the locking screws with non-locking ones reduces the maximum stress in the plate by 62.5% and in the screw by 8.8%, respectively. Considering displacement in screws, findings indicate that the superiority of locking or non-locking screws depends on their location relative to the fracture.

The modeling and simulation of the next-generation of therapeutic and diagnostic systems (i.e. theranostics) will involve hybrid nanosystems: a combination of two or more nanoparticles. The release rate of this may be studied in silico and atomistically to guide experimentalist in the design of such drug-delivery systems. Here, classical atomistic molecular dynamics was used to investigate the expected shape, long term stability and release rate of 5-fluororacil molecules loaded onto Au doped Fe₃O₄nanoparticles. The binding energy (BE) of three potential shapes were used to identify the shape that is the most stable and therefore the most likely to be synthesized. Hexagonal nanoparticles (7 ± 2 nm) were identified in the simulations as the most stable with BE per molecule ∼ -1.1 eV (-25 kcal mol^-1). Furthermore, to support our theoretical observations, Au-Fe₃O₄nanoparticles were synthesized and characterized with the aid of TEM, XRD, FTIR and VSM. TEM confirmed the size distribution 1-12 nm and the release at pH 5.7 reached 84% vs 41% at pH 7.4 within 48 h.

The classification of motor imagery-electroencephalography (MI-EEG) is a growing research field in brain-computer interface, which allows people with motor disabilities to communicate with the outside world through assistive devices. Although deep learning-based models have revolutionized MI-EEG decoding, dealing with the MI-EEG signals remains challenging due to the signals being non-stationary, containing noisy signals, and having a low signal-to-noise-ratio. This study proposes to employ a novel EEG channel-wise attention module (ECWAM) in a deep convolutional neural network (deep CNN) to enhance the accuracy of MI-EEG decoding. The proposed method calculates the channel score for each mu band EEG channel and amplifies the prominent EEG channels based on their channel scores. The proposed method is evaluated on 54 subjects, binary class MI dataset from the Korea University EEG dataset. Additionally, the proposed method is compared with the conventional channel-wise attention module mentioned in the literature. The results for the hold-out analysis outcomes suggest that the proposed deep CNN with ECWAM has statistically improved the average classification accuracy of the baseline deep CNN model from 63.96% to 68.98%, withp-value = 0.02 for the subject-specific MI classification. Further, the scalp map of the EEG channel ranking obtained by the proposed method and the conventional channel-wise attention module mentioned in the literature is also compared. The results of the comparison show that the proposed method yields a higher channel ranking in the brain's motor cortex region, which is the primary contributing area for MI activity.