Journal of Medical Engineering and Technology最新文献

Electrophoretic tissue clearing has been a commonly used laboratory method since the early twentieth century. Infrastructure for standard procedures has yet to be formed. In particular, control of the heat produced by electrophoresis, the voltage applied to the electrodes, the resistance, and the speed of liquid circulation create difficulty for researchers. We aimed to develop a compact organ electrophoresis system that enables the researcher to have easy, rapid, and inexpensive working opportunities. The system includes an electronic control unit, a liquid tank, a temperature control unit, and an electrophoresis chamber. The control unit software can keep the system stable by using information on temperature and circulation rate received through the sensors using the feedback principle. Corrosion and particle collection are reduced to a minimum as platinum wires are used for electrophoresis electrodes. A temperature control unit can heat and cool via a liquid tank base. The CORES is an all-in-one, easy-to-use solution for electrophoretic tissue clearing. It assures efficient, rapid, and consistent tissue clearing. The system was stable with 72 h of continuous operation. Patent applications and trial version studies for introducing the system to researchers are still in progress.

It is known that the geometric structures of bones are very complex. This has made researchers unable to model them with the continuum approach and suffice to model them with simulation or experimental tests. Undoubtedly, provide a simple and accurate continuum model for studying bones is always desirable. In this article, as the first serious endeavour, a suggested beam model is investigated to see whether it is suitable for modelling femur bones or not. If this model gives an acceptable answer, it can be a link to the continuum theories for beams. In other words, the approximated beam model can be formulated with continuum approach to study femur bone. For feasibility study of the approximated model for femur bones, both static and dynamic analysis of them are investigated and compared. It is found that in most cases for vibration analysis, the suggested model has acceptable results but in static analysis, the mean difference between the results is about 16%. This research is hoped to be the first serious step in this category.

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.

This study presents a solid approach for small-scale medical oxygen production unit using pressure swing adsorption (PSA) technology. The objective of this research is to develop a mathematical model and conduct a sensitivity analysis to optimise the design and operating parameters of the PSA system. Based on the simulation results, an optimal set of operational parameter values has been obtained for the PSA beds. The result shows that the binary system produced oxygen with a purity of 94%, at the adsorption pressure 1 bar and temperature of 308K. The findings demonstrate the effectiveness of the proposed small-scale PSA system for medical oxygen production, highlighting the impact of key parameters and emphasising the need for careful optimisation. The findings serve as a guide for the design and operation of small-scale PSA systems, enabling healthcare facilities to produce their own medical oxygen, thereby improving accessibility and addressing critical shortages during emergencies. Future research may explore the integration of large scale PSA units in hospitals in Morocco.

Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries.

Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.

Although telehealth, and in particular RPM, have demonstrated to drive many benefits, such as reduction in cost and hospital-acquired infections, previous research has shown many usability challenges when patients operate a medical device without supervision of a medical professional. To combat this issue, the Pi-CON methodology is applied to develop a novel sensor with the objective to continuously acquire a patient's vital signs from a distance, without the need to attach any markers or sensors to the patient, and with limited user interaction required. Pi-CON stands for passive, continuous and non-contact, and describes a way to improve the user experience for patients or caregivers that have a need to perform a vital signs measurement themselves, without the presence of a medical professional. The developed sensor utilises radar and optical sensing technologies and transmits acquired data to a cloud-based service where it can be viewed in near real-time by the patient or family members from anywhere via an intuitive user interface. This user interface, as well as the sensor itself were designed based on design needs and requirements to adhere to the user-centered design process. The development of the sensor, including utilised technologies, components, and the user interface are presented, including inspirations for future work.