Medical Engineering & Physics最新文献

Biplanar videoradiography (BVR) is a gold-standard technique for quantifyingin vivobone motion, yet the influence of x-ray image resolution on pose estimation accuracy remains unexplored. This study investigates how downsampling x-ray images impacts model-based pose estimation, using high-speed BVR data from a participant with implanted tantalum beads. Images were downsampled from 2048 × 2048 to 512 × 512 using bicubic and nearest-neighbour interpolation. Across multiple bones and varying perturbation levels, downsampling significantly reduced rotational and translational errors when compared to full-resolution images for both interpolation results. Bicubic interpolation led to slightly improved pose accuracy for certain bones, demonstrating enhanced edge clarity that benefits the optimisation algorithm. Pose estimates for full-resolution images exhibited more outliers and greater variability for all the bones investigated. These findings highlight that downsampling images improves pose estimation accuracy even for challenging anatomical areas such as the ankle. We recommend bicubic downsampling to 512 × 512 pixels as a best practice for BVR tracking of the ankle complex, when using both automated optimisation and manual workflows.

Several image processing methods in Dermatology are grounded in shallow and deep learning approaches. These solutions are relevant to assist health experts in decision-making processes related to harmful melanoma-a malignant melanocytic condition-and other skin lesions. This work aims to compare these approaches in a specific classification problem: malignant melanocytic lesions versus non-melanocytic ones. We developed 39 learning method configurations, including three original ones based on fine-tuned deep neural networks. Some implemented settings incorporate auxiliary procedures, such as oversampling, feature selection and data augmentation. An experimental evaluation in the public Derm7pt dermoscopic database suggests that the best original setting performance was competitive against the leading results reported by recent literature alternatives. In particular, the proposal reached average accuracy and sensitivity of 0.9909 and 0.9976, respectively. These results were averaged across three runs of the stratified nested cross-validation strategy. Moreover, our 39 configurations outperformed an experimental baseline derived from the majority class error. Thus, this work can be helpful in inspiring computational systems that could act as preliminary filters to support the detection of a harmful form of skin cancer and its separation from other lesions.

Supercooling is gaining recognition as a promising technique for preserving biological materials at subfreezing temperatures, offering a key advantage over traditional freezing by preventing harmful ice formation. However, because supercooling represents a metastable thermodynamic state, it is susceptible to uncontrolled ice nucleation. Research suggests that maintaining isochoric (constant volume) conditions may enhance the stability of supercooled systems compared to isobaric (constant pressure) conditions. During transportation by land, sea, or air, supercooled systems are often exposed to vibrations and high accelerations. This study aims to assess whether isochoric conditions can improve the stability of supercooled systems under typical external stressors encountered during transportation, compared to isobaric conditions. Using an isochoric nucleation detection device, we measured the probability of nucleation in 5.5 ml volumes of supercooled water subjected to vibrations of 50-60 Hz and accelerations of 6 g under both conditions. The results revealed that, under isobaric conditions, these stressors increased the average nucleation temperature from -8 °C to -4 °C. In contrast, under isochoric conditions, the nucleation temperature remained at -8 °C. This suggests that isochoric supercooling may offer significant advantages for transportation. However, further research is needed to explore the effects of specific vibration frequencies, accelerations, and container designs to optimize performance for various transportation modes.

Precisely recognizing and classifying physical activity in the everyday routines of patients with chronic illnesses can facilitate the implementation of precision medicine in the treatment of conditions like diabetes. Human activity recognition (HAR) is crucial in ubiquitous computing, specifically in the management of chronic diseases such as diabetes. Deep learning architecture have been increasingly popular for sensor-related HAR in recent years, demonstrating impressive performance. Nevertheless, they encounter obstacles when extracting and characterizing features, as well as segmenting continuous actions, particularly when working with time series data. These issues are particularly relevant in the field of diabetes management, where precise tracking of physical activity is crucial for effective therapy and the control of blood glucose levels. This paper presents a multichannel fusion model which integrates a mutichannel convolutional neural network (CNN) and a bidirectional gated recurrent unit (Bi-GRU) with thebahdanauattention mechanism, terminated with extra trees classifier. This model is designed to leverage the strengths of CNN, BiGRU and integration of attention mechanism for comprehensive feature extraction and temporal relationship learning. The efficiency of different machine learning classifiers evaluated by cross-validation to determine the best effective method for the specific task. The performance of the proposed architecture was evaluated using the UCI-HAR dataset. The model achieved an accuracy of 99.52%, precision of 99.56%, recall of 99.55%, andF1 score of 99.55% when combined with the extra trees classifier in the proposed fusion architecture which is better compared to existing models in recognizing undeclared physical activity types.

Cancer stem cells (CSCs) are neoplastic cells that possess certain stem cell properties and are increasingly recognized as pivotal contributors to cancer metastasis, recurrence, and therapy resistance. Recent studies have demonstrated that metformin, a medication for type 2 diabetes, can inhibit the proliferation of CSCs. Malignant melanoma, which harbors CSCs, serves as a valuable model for evaluating cancer therapies and characterizing CSCs. This study assessed the impact of metformin (0-500µg ml^-1) on melanoma CSCs by employingin vitrothree-dimensional (3D) cell cultures and optical microscopy. Our findings revealed that higher concentrations (24 mM) of metformin corresponded to a reduced number of cell spheres, consistent with results reported by other research groups. These observations suggest that optical microscopy is a viable technique for monitoring the short-term effects of metformin on melanoma CSCsin vitro.

Clots will always result in the slowing or complete blockage of intravascular blood flow, which finally cause the ischemic disease. Mechanical thrombectomy (MT), as a novel interventional therapy, is gaining prominence in clinical practice. The process parameters during MT directly affect the clot cutting and fracture failure characteristics. This study utilized porcine blood to create a clot model for cutting experiments, investigating the effects of cutting process and tool structure on the clot cutting failure characteristics. The results indicate that both cutting speed and tool structure significantly affect cutting force and tool displacement, with tool structure being the predominant factor. When the cutting speed varies from 10 mm min^-1to 400 mm min^-1, the cutting force reaches its minimum at 220 mm min^-1, with a maximum reduction of approximately 5 N. The tool with larger rake angle exhibit the greatest influence on cutting force, while the presence of inclination significantly increases the deformation displacement of the tool. These findings provide valuable insights for optimizing the design of MT devices, ultimately enhancing the efficiency and safety of thrombus procedures.

Mitral valve prolapse (MVP) is a prevalent cardiac disorder affecting approximately 2%-3% of the population. Accurate early diagnosis is essential to prevent progression into more severe conditions. This study introduces a non-invasive methodology for assessing MVP severity using phonocardiogram signals analyzed through bispectral (third-order spectral) techniques. MVP signals were categorized into four types based on murmur intensity and the presence of an ejection click (EC). Following wavelet-based denoising and energy-based segmentation, the energetic ratio (ER%) was computed as a clinical indicator of severity. Bispectral analysis was then applied to extract higher-order spectral (HOS) features including bispectral magnitude, entropies, spectral moments, and the weighted bispectrum center. These features were analyzed to distinguish between severity categories and correlate with murmur energy. An ANOVA test was conducted to assess the statistical significance of each feature and its discriminative power.

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.