Critical Reviews in Biomedical Engineering最新文献

The COVID-19 pandemic, emerging/re-emerging infections as well as other non-communicable chronic diseases, highlight the necessity of smart microfluidic point-of-care diagnostic (POC) devices and systems in developing nations as risk factors for infections, severe disease manifestations and poor clinical outcomes are highly represented in these countries. These POC devices are also becoming vital as analytical procedures executable outside of conventional laboratory settings are seen as the future of healthcare delivery. Microfluidics have grown into a revolutionary system to miniaturize chemical and biological experimentation, including disease detection and diagnosis utilizing μPads/paper-based microfluidic devices, polymer-based microfluidic devices and 3-dimensional printed microfluidic devices. Through the development of droplet digital PCR, single-cell RNA sequencing, and next-generation sequencing, microfluidics in their analogous forms have been the leading contributor to the technical advancements in medicine. Microfluidics and machine-learning-based algorithms complement each other with the possibility of scientific exploration, induced by the framework's robustness, as preliminary studies have documented significant achievements in biomedicine, such as sorting, microencapsulation, and automated detection. Despite these milestones and potential applications, the complexity of microfluidic system design, fabrication, and operation has prevented widespread adoption. As previous studies focused on microfluidic devices that can handle molecular diagnostic procedures, researchers must integrate these components with other microsystem processes like data acquisition, data processing, power supply, fluid control, and sample pretreatment to overcome the barriers to smart microfluidic commercialization.

Engineers play a critical role in the advancement of biomedical science and the development of diagnostic and therapeutic technologies for human well-being. The complexity of medical problems requires the synthesis of diverse knowledge systems and clinical experiences to develop solutions. Therefore, engineers in the healthcare and biomedical industries are interdisciplinary by nature to innovate technical tools in sophisticated clinical settings. In academia, engineering is usually divided into disciplines with dominant characteristics. Since biomedical engineering has been established as an independent curriculum, the term "biomedical engineers" often refers to the population from a specific discipline. In fact, engineers who contribute to medical and healthcare innovations cover a broad range of engineering majors, including electrical engineering, mechanical engineering, chemical engineering, industrial engineering, and computer sciences. This paper provides a comprehensive review of the contributions of different engineering professions to the development of innovative biomedical solutions. We use the term "engineers in medicine" to refer to all talents who integrate the body of engineering knowledge and biological sciences to advance healthcare systems.

Ischemic disease has severe impact on patients which makes accurate diagnosis vital for health protection. Improving the quality of prediction of patients with ischemic extremity disease by using hybrid fuzzy model allows for early and accurate prognosis of the development of the disease at various stages. The prediction of critical ischemia of lower extremity (CLI) at various disease stages is complex problem due to inter-related factors. We developed hybrid fuzzy decision rules to classify ischemic severity using clinical thinking (natural intelligence) with artificial intelligence, which allows achieving a new quality in solving complex systemic problems and is innovative. In this study mathematical model was developed to classify the risk level of CLI into: subcritical ischemia, favorable outcome, questionable outcome, and unfavorable outcome. The prognosis is made using such complex indicators as confidence that the patient will develop gangrene of the lower extremity (unfavorable outcome), complex coefficient of variability, and reversibility of the ischemic process. Model accuracy was calculated using representative control samples that showed high diagnostic accuracy and specificity characterizing the quality of prediction are 0.9 and higher, which makes it possible to recommend their use in medical practice.

This paper presents a review of studies on the effects of local vibration training (LVT) on muscle strength along with the associated changes in neuromuscular and cell dynamic responses. Application of local/direct vibration can significantly change the structural properties of muscle cell and can improve muscle strength. The improvement is largely dependent on vibration parameters such as amplitude and frequency. The results of 20 clinical studies reveal that electromyography (EMG) and maximal voluntary contraction (MVC) vary depending on vibration frequency, and studies using frequencies of 28-30 Hz reported greater increases in muscle activity in terms of EMG (rms) value and MVC data than the studies using higher frequencies. A greater muscle activity can be related to the recruitment of large motor units due to the application of local vibration. A greater increase in EMG (rms) values for biceps and triceps during extension than flexion under LVT suggests that types of muscles and their functions play an important role. Although a number of clinical trials and animal studies have demonstrated positive effects of vibration on muscle, an optimum training protocol has not been established. An attempt is made in this study to investigate the optimal LVT conditions on different muscles through review and analysis of published results in the literature pertaining to the changes in the neuromuscular activity. Directions for future research are discussed with regard to identifying optimal conditions for LVT and better understanding of the mechanisms associated with effects of vibration on muscles.

To construct a three-dimensional (3D) model of a tooth, multiple charge coupled device (CCD) cameras should be deployed in practice. Each CCD camera captures part of the tooth from a different angle. The images captured by different cameras must be registered to construct the relational 3D model. Sample consensus initial alignment (SAC-IA) algorithm is usually adopted, and fast point feature histograms (FPFH) descriptor is selected to calculate eigenvalues for different images. However, the original SAC-IA algorithm cannot satisfy a real-time application because of low efficiency and accuracy. According to the application of voxel nearest neighbor search in octree in 3D data search, this paper proposes an improved SAC-IA algorithm based on voxel nearest neighbor search to improve the efficiency and accuracy of the algorithm. The experimental results show that comparing to the traditional SAC-IA algorithm, the proposed algorithm based on voxel nearest neighbor search improves the efficiency by 20.95% and the registration accuracy by 24.95%. The improved algorithm can be deployed to construct a 3D model of a tooth as well as 3D model construction of other objects based on coded structured light.

Countermovement jump (CMJ) is frequently used to assess the neuromuscular capacity in athletes and track adaptations to training, typically through outcome variables such as jump height, peak/mean force, power or velocity, and rate of force development. Recently, there has been an increasing interest to analyze the shape of the force-time curve of the CMJ and its relationship to CMJ performance. This aim of the present review was to collect and analyze the available literature pertaining to this topic. One approach to analyze CMJ curve shape is to classify it as "unimodal" or "bimodal," based on the number of force peaks. The difference between athletes showing unimodal and bimodal curves is negligible in terms of jump height, while unimodal curves are associated with higher reactive strength index. Rather than the number of peaks, the most important characteristics that maximizes CMJ height seems to be the temporal alignment of peak force with the instant of the lowest center-of-mass position (i.e., when the jumper transitions from the braking to the propulsive phase). Other than bimodal/unimodal classification, the "shape factor" (the value of force impulse, divided by the area of the rectangular shape drawn around) has been emerging as another approach to assess CMJ curve shape; however, the studies exploring its relationship with performance are few and inconclusive.

In recent years, diabetic retinopathy (DR) needs to be focused with the intention of developing accurate and effective approaches by accomplishing the existing challenges in the traditional models. With this objective, this paper aims to introduce an effective diagnosis system by utilizing retinal fundus images. The implementation of this diagnosis model incorporates 4 stages like (i) preprocessing, (ii) blood vessel segmentation, (iii) feature extraction, as well as (iv) classification. Originally, the median filter as well as contrast limited adaptive histogram equalization (CLAHE) help to preprocess the image. Moreover, the Fuzzy C Mean (FCM) thresholding is applied for blood vessel segmentation, which generates stochastic clustering of pixels to obtain enhanced threshold values. Further, feature extraction is accomplished by utilizing gray-level run-length matrix (GLRM), local, and morphological transformation-based features. Furthermore, a deep learning (DL) model known as convolutional neural network (CNN) is employed for the diagnosis or classification purpose. As a main novelty, this paper introduces an optimal feature selection as well as classification model. Further, the feature selection is done optimally by FireFly Migration Operator-based Monarch Butterfly Optimization (FM-MBO) which hybridized of the monarch butterfly optimization (MBO) and fire fly (FF) algorithms as the entire adopted extracted features attain higher feature length. Moreover, the proposed FM-MBO algorithm helps for optimizing the count of CNN's convolutional neurons to further improve the performance accuracy. At the end, the enhanced outcomes of the adopted diagnostic scheme are validated via a valuable comparative examination in terms of significant performance measures.