Medical Engineering & Physics最新文献

Radiculopathy is a painful condition characterized by nerve root (NR) compression. NRs possess unique anatomical and biomechanical features, including the absence of protective layers, making them particularly vulnerable to deformation. This review aims to synthesize current knowledge of NR biomechanics, elucidate the mechanisms linking compressive loading to radicular pain, and identify literature gaps. A search of PubMed and Scopus was conducted with search terms targeting NR biomechanics and pain. Two reviewers independently screened 2658 titles, abstracts, and texts, identifying 53 studies that met the inclusion criteria. Current evidence underscores the role of mechanical stress in radiculopathy, with studies identifying compression thresholds that disrupt NR function. Key anatomical culprits include intervertebral discs, ligaments, and vertebrae. Research highlights the viscoelastic nature of NRs, which may amplify dysfunction under chronic loading and lead to ectopic firing and persistent pain. Despite these insights, considerable gaps remain in linking precise biomechanical thresholds to symptoms. Advancing this field requires further knowledge on nervous tissue mechanical properties. With further knowledge of tissue behavior, integration of state-of-the-art technology could explore the interplay of loading and NR responses. A deeper understanding of mechanisms could revolutionize diagnostics and offer tailored interventions to alleviate pain and improve patient outcomes.

Arteriovenous shunts created during hemodialysis are common sites of stenosis. While shunt (blood flow) sounds may be potential indicators in developing noninvasive, simple stenosis screening methods, previous studies have not shown sufficient detection accuracy for practical stenosis screening applications. Despite several studies aiming to improve stenosis screening methods, the detailed mechanism of shunt sound generation remains unclear. We aimed to clarify the mechanism of shunt sound generation to aid the development of a stenosis screening method using shunt sounds. We analyzed flow in a shunt blood vessel model using particle image velocimetry. Spectral analysis of vorticity magnitude fluctuations, which are considered candidate sources of shunt sounds, revealed that stenosis increased the high-frequency spectrum above 400-500 Hz by approximately 5-10 dB Hz^-1. A similar trend was observed in the vorticity magnitude fluctuations and shunt sound spectra, suggesting that vorticity magnitude fluctuations downstream of the stenosis contribute to shunt sound generation. Moreover, the results suggest that stenosis can be accurately detected by collecting shunt acoustic data at multiple points downstream of the shunt branch and comparing the acoustic spectrum at each point with those upstream and downstream. These findings contribute to the early detection and treatment of intradialytic shunt vascular stenosis.

This paper presents the development of a mathematical model as an initial step toward calculating the surface pressure between a patient and an air-cell seat cushion, based on the barometric internal pressure of the cushion. The model was developed and validated using measurements from a single air cushion. A total of 36 measurement series were recorded under varying initial and ambient pressures. Each series included 11 force levels ranging from 50 to 150 N, applied to the air cushion using a controlled force test bench. Alongside the barometric internal and ambient pressures, the surface pressure was measured with a pressure plate from T&T medilogic Medizintechnik GmbH (Schönefeld, Germany). The barometric internal pressure was mathematically separated into two parameters: the initial internal pressure and the pressure difference induced by loading. Ten different functions were trained with half of the measurement series and validated with the other half, varying in complexity and parameter weighting. The best-performing model achieved an R²value of 0.98, with an average deviation of -1.9 ± 3.7% and a maximum error of 10.7%. These results are promising and comparable to the measurement accuracy of established pressure mat systems, supporting the model's potential as a foundation for future active wheelchair cushion applications.

Fundus image disease diagnosis and quality assessment have emerged as essential tasks in medical image analysis. High-quality fundus images provide clear and well-defined pathological features, thereby enhancing diagnostic performance; similarly, diagnostic capability is regarded as one of the key criteria for assessing fundus image quality (FIQ). Motivated by this observation, we propose a dual-task collaborative optimization network (DTCONet) to explore the intrinsic interplay between quality assessment and disease diagnosis, enabling mutual promotion and performance enhancement. Specifically, DTCONet adopts a dual-branch feature extraction framework to strengthen the model's perception of fine structures and pathological characteristics. A dual-task module is then designed to process quality assessment and disease diagnosis in parallel by sharing fundus image representations. Furthermore, a collaborative optimization module is introduced to fully exploit the strong correlation between the two tasks. This study provides new insights into the relationship between FIQ assessment and disease diagnosis. Extensive quantitative and qualitative experiments on five widely used medical image datasets demonstrate the effectiveness and generalizability of the proposed DTCONet.

Magnetic anchoring technology solves the congestion at the puncture site of minimally invasive surgery by fixing the camera system with magnetic force. However, traditional laparoscopic systems use electromagnetic motors to drive and have magnetic field interference problems. Combining magnetic anchoring technology with ultrasonic motors, this system creates a novel laparoscopic imager for single-port surgery. The ultrasonic motor operates via the stator's resonant elliptical motion, and the overall design addresses the common challenges of instrument interference and a restricted visual field. The system utilizes ultrasonic motors as the drive sources to control the rotation and translation of the laparoscope, respectively. Finite element simulations were used to analyze the motor design, and the operating modes and harmonic response characteristics of the ultrasonic motors were validated by experiment. The system incorporates a magnetic anchoring module, an image zoom module, and a pressure sensing module, supporting three degrees of freedom in translation, 160° rotation, and 6 mm lens zoom. Thermal imaging confirmed that the magnetic-assisted laparoscopic temperature remained below 40 °C throughout a 160 s operation, with bionic experiments conducted to verify its overall feasibility and safety in a simulated surgical environment. This research provides a high-degree-of-freedom, electromagnetic interference-free solution for single-port laparoscopic surgery, demonstrating significant clinical application potential.

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.