Journal of Medical Engineering and Technology最新文献

Wireless Body Area Networks (WBANs) are vital for real-time health monitoring in eHealth systems. This article presents a comprehensive comparative analysis of MAC protocols based on IEEE 802.15.6 and IEEE 802.15.4 standards, with a focus on energy efficiency, latency, reliability, and emergency data handling. We critically examine superframe structures, access mechanisms, and adaptive MAC designs, and introduce a five-dimensional framework for protocol evaluation. Our study identifies key limitations in existing solutions-such as lack of support for emergency traffic and mobility adaptation-and outlines future research directions for developing intelligent, QoS-aware, and energy-efficient MAC protocols tailored to heterogeneous WBAN environments.

Tremors are defined as low to medium-frequency oscillations of the human limbs. These tremors are typically a result of chemical imbalances in the brain that lead to involuntary or uncontrolled voluntary movements of the human arm. Numerous medical treatments have been devised to control tremors, but they can be unsuccessful and expensive, with some undesirable side effects in the long run. This paper introduces a passive actuator capable of attenuating tremors over a wide range of frequencies while being lightweight and small in size. The tremors are modelled as harmonic vibrations, and the arm is modelled as a lumped mass for the shoulder flexion-extension degree of freedom. The device produces $99.34\%$ tremor reduction at resonance and an average tremor reduction of $55\%$ between 0.8 and 8Hz.

Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosis is critical to improve patient outcomes and reduce the risks of overtreatment or missed detection. Conventional diagnostic approaches, including prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological analysis, often suffer from limited sensitivity and specificity, leading to false positive or delayed diagnosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as an effective modality for prostate cancer detection, providing complementary anatomical and functional information. This study proposes a novel hybrid diagnostic framework that integrates Generalized Quantum Gamma Polynomial (GQGP) features, kinetic signal intensity features, and deep learning-based representations. GQGP features capture subtle intensity variations and quantum-inspired statistical characteristics, while kinetic features quantify contrast-enhancement dynamics to discriminate malignant from benign tissues. These handcrafted descriptors are fused with high-level features extracted using convolutional neural networks (CNNs) to construct a comprehensive feature representation. Experimental evaluation on publicly available prostate imaging datasets demonstrates that the proposed fusion framework significantly outperforms single-feature and traditional methods, achieving a classification accuracy of 97.32%. The results highlight the effectiveness of combining mathematical modeling, radiomics, and artificial intelligence for improved prostate cancer diagnosis.

This scoping review mapped the literature on alternative techniques for removing fractured screws from dental implants. Following the five-step methodological framework by Arksey and O'Malley and the Joanna Briggs Institute Manual for Evidence Synthesis, the study adhered to the PRISMA-ScR checklist. The protocol was registered in the Open Science Framework (). Two independent reviewers searched MEDLINE (PubMed), Web of Science, Embase, and ClinicalTrials.gov in December 2024 using the terms "dental implants" AND ("screw retrieval" OR "fractured screw" OR "screw removal" OR "screw fragment"), including gray literature and reference lists. Among the 47 included studies, six were in vitro, one in silico, twenty-six clinical case reports, and fourteen technical descriptions. The main removal approaches identified were: (1) manual instruments; (2) ultrasonic devices; (3) mechanical or rescue kits; (4) rotary or drilling methods; and (5) customized alternatives such as laser welding, hollow screw modification, and guided drilling. No single method proved superior. The choice of technique depends on clinical conditions, fracture type, and implant preservation. Conservative, low-risk approaches should be attempted before invasive methods. Overall, prevention, torque control, and periodic maintenance remain the most effective strategies to avoid screw fractures. .

Phonocardiography (PCG) plays a fundamental role in the diagnosis of heart valve diseases, but it has certain limitations. The signals are often affected by noise and variations, which makes their analysis more complex. Furthermore, human hearing does not always allow for the perception of all the sounds, thus increasing the risk of diagnostic errors. The non-stationary nature of cardiac signals also contributes to these difficulties. This paper presents a hybrid method for discriminating valvular diseases from PCG signals, using a dataset of 32 recordings divided into five categories: aortic stenosis (AS), mitral regurgitation (MR), mitral valve prolapse (MVP), and ejection click (EC), and normal cases (N). After denoising with the discrete wavelet transform (DWT), the features extracted from the PCG signals were processed using principal component analysis (PCA) to select the most relevant ones. The analysis of these features enabled the differentiation of several valvular heart diseases. The methodology achieved effective discrimination between pathological conditions using K-means clustering, with three principal components explaining 91% of the total variance. The energy ratio (ER), murmur duration (ΔTM), and inverse approximation signal ratio (InASR) emerged as the most discriminative features. The results demonstrated strong clustering performance, with a Silhouette Score of 0.5829, a Davies-Bouldin Index of 0.5798, a Within-Cluster Sum of Squares (WCSS) of 1.1403, and a Calinski-Harabasz Index of 54.17, achieving an overall accuracy of 93.3% in discriminating between the different valvular heart diseases, particularly aortic stenosis, mitral regurgitation, and mitral prolapse. Overall, this approach paves the way for the development of automated diagnostic tools, enhancing both the precision and speed of patient diagnosis.

Timely recognition of dermatological manifestations caused by toxic environmental exposure is vital for effective healthcare management. Arsenic, a widespread contaminant in groundwater, has severe dermatological effects, leading to chronic disorders that often remain undiagnosed in their early stages. This study presents an advanced deep learning framework designed to support the early diagnosis of arsenic-induced skin conditions through dermoscopic image analysis. The research utilised a comprehensive dataset of 8892 dermoscopic images collected from four field sites in Bangladesh, encompassing both arsenic-exposed and unaffected individuals. Discriminative image features were extracted using a synergistic ResNet-DenseNet architecture, which captures both local textural and global contextual representations. The extracted features were subsequently classified using the k-Nearest Neighbour algorithm to distinguish arsenic-affected from healthy skin images. The proposed method achieved 99.37% classification accuracy, a 99.36% F1-score, 99.14% sensitivity and 99.59% recall, reflecting its strong diagnostic reliability. These outstanding results suggest that the framework can efficiently assist dermatologists by providing automated, consistent and objective evaluation of arsenic-related lesions. It also provides a data-driven method for monitoring public health in areas where arsenic contamination is a long-term problem. Overall, the study demonstrates the clinical potential of deep learning-based dermoscopic analysis for improving the early detection and management of arsenic-related dermatological disorders.

Purpose: Analysis of the fit of off-the-shelf knee endoprostheses in three-dimensional planes, with possible impact on the implantation results.

Methods: The implantation of three different off-the-shelf knee endoprostheses is simulated in 92 patients who were treated with custom-made knee endoprostheses in Agaplesion Ev. Klinikum Schaumburg joint centre Fit was determined in different planes using newly defined measurement variables.

Results: Significant deviation of fit in different measurement categories depending on prothesis model and patient characteristics.

Conclusions: The results of this study encourage to do preoperative analysis of patients anatomical knee shape and to perform preoperative fit simulations in defined measurement categories for different knee endoprotheses before implantation to reach optimal results.

Clinical relevance: Such algorithms may significantly improve the early postoperative results in terms of range of motion and long-term revision rates, with an impact on patient satisfaction and overall treatment costs for knee arthritis.

Wearable devices are used increasingly within the medical world, ranging from monitoring for head trauma to screening for heat injuries. Understanding what makes these devices tolerable to the end user in remote hostile environments is crucial for research, military, and humanitarian medicine, with broader translational implications. This opportunistic qualitative study trialled five different forms of wearable devices on the Interdisciplinary South Pole Innovation and Research Expedition 2022 (INSPIRE 22), an expedition which skied from the edge of the Antarctic land mass to the South Pole. It also examined the feasibility of near-real time analysis of wearable data from a hostile environment remotely from the UK. Key findings highlighted that usability of wearable devices was impacted by human-device interface factors (comfort, user buy in, and charging) and device resource requirements (power, data, and storage space on a personal mobile phone). Users required a more positive than negative aspects to maintain device interaction. Near-real-time data analysis of wearable technology from extreme environments is feasible but only on a small inconsistent scale due to limited connectivity. Reliable internet access, broader bandwidth, and better user access to data are essential to achieve meaningful health and performance insights for the individual and wider organisations.

Tooth cavities are primarily driven by sugar-induced bacterial activity that progressively erodes dental structures. Advances in medical image processing provide dentists with valuable tools to support accurate diagnosis and selection of appropriate therapeutic interventions, thereby improving oral healthcare. This study presents the development of an automated dental disease detection system, designed to reduce clinician workload, minimise diagnostic time, and lower the risk of human error. Dental radiographs are first subjected to noise reduction, greyscale conversion, filtering, and resizing, followed by the extraction of discriminative features. Key feature extraction methods include Wavelet analysis, Gray-Level Co-Occurrence Matrix (GLCM), and texture analysis. These features were subsequently used to train and evaluate machine learning classifiers, specifically Support Vector Machine (SVM) and Neural Network (NN) models. The system achieved classification accuracies of 80% with SVM and 77% with NN when all features were combined. The primary objective of the system is to classify dental X-ray images as normal or abnormal, and to further identify abnormalities such as caries. Compared to conventional diagnostic methods, the proposed automated approach enables faster and more reliable detection of dental diseases. Ultimately, this system has the potential to support dentists in clinical decision-making and enhance the quality of patient care.

Modern dance places relatively high requirements on dancers' balance ability, which can be enhanced through certain training. This paper mainly investigated the effects of resistance training on the balance and technical performance of female modern dancers. Forty female modern dancers from the Dance College of Northwest Normal University were randomly assigned to the instability resistance training (IRT) group or the resistance training (RT) group to undergo a 12-week training program. Balance ability and technical performance were assessed before and after the training. After the training, the balance ability and technical performance of both the IRT group and the RT group were affected to a certain extent. Specifically, the closed-eye one-legged standing time for the left and right legs in the IRT group was 37.74 ± 20.16 s and 42.36 ± 16.87 s, respectively (p < 0.05 compared to pre-experiment and the RT group). Moreover, all indices of dynamic standing stability in the IRT group showed improvement (p < 0.05 compared to pre-experiment and the RT group), and the balance move scores for the IRT group also improved significantly, with the seated low-space near-ground rotation score reaching 8.37 ± 0.56 points (p < 0.05 compared to pre-experiment and the RT group). The results demonstrate that IRT has an advantage in improving the balance ability and technical performance of female modern dancers. This method can be effectively applied in modern dance training programs. Keywords: resistance training, modern dance, technical performance, balance ability.