Journal of Medical Engineering and Technology最新文献

The human factors engineering (HFE) process supports the design and development of medical devices, especially novel devices requiring clinical investigation. The typical culmination of the HFE process prior to market approval is a human factors (HF) validation study, with specific requirements of participant, environment and task representation that carry a financial and temporal burden for medical device manufacturers. Whilst strongly recommended ahead of clinical investigations by regulators (and the authors), the prescribed methodology for HF validation studies required for pre-market approval may be excessive ahead of a clinical investigation during the development process. However, the stringent nature of HF validation studies will support effective clinical investigation design and minimise risks of poor clinical outcome or compliance. This paper provides recommendations in what to consider when determining what type of HF study to conduct ahead of each clinical investigation phase as well as insights into the symbiotic benefits of HFE and clinical investigations.

Climate change has amplified the importance of continuous and precise body core temperature (T_core) monitoring in the everyday life. In this context, assessing T_core through ingestible capsules technology, i.e., gastrointestinal temperature (T_{gastrointestinal}), emerges as a good alternative to prevent heat-related illness. Therefore, we conducted a systematic review to point out values of normal T_{gastrointestinal} measured through ingestible capsules in healthy humans. The study followed PRISMA guidelines and searched the PubMed and Scielo databases from 1971 to 2023. Our search strategy included the descriptors ("gastrointestinal temperature") AND ("measurement"), and eligible studies had to be written in English and measured T_{gastrointestinal} using ingestible capsules or sensors in healthy adults aged 18-59 at rest. Two pairs of researchers independently reviewed titles and abstracts and identified 35 relevant articles out of 1,088 in the initial search. An average value of 37.13 °C with a standard deviation of 0.24 °C was observed, independently of the gender. The values measured ranged from 36.70 °C to 37.69 °C. In conclusion, this systematic review pointed out the mean value of 37.13 ± 0.24 °C measured by ingestible capsules as reference for resting T_{gastrointestinal} in healthy adult individuals.

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.

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.