Journal of Medical Engineering and Technology最新文献

The COVID-19 pandemic has seen the advent of novel medical devices and practices. Demand for quality healthcare services rose exponentially which eventually led to accessibility becoming a major issue of concern. In addition to this, in-person consultations and various other conventional treatment methods were proven to be problematic. Limitations of traditional health care systems such as in-person consultations were highlighted, and conventional treatment methods have proven to be problematic. As an alternative approach, telehealth services are now gaining recognition due to their high efficiency, ease of use, and state-of-the-art technology. In this article, trends of telemedicine and its evolving popularity across the medical community due to the pandemic and beyond are studied and highlighted. An online survey form was circulated to 42 medical practitioners and interns to analyse the growing interest in telemedicine. The questionnaire covered the physicians' perspectives, preferences, experiences, and other important aspects of home-based teleconsultation. Based on the responses collected from doctors and medical interns, 14.2% disapproved, whereas 38.1% favoured and 47.6% showed a neutral response to the teleconsultation. More than 50% of the respondents claim the process to be time-consuming and 42% of them perceived it to be the other way round. 4.8% of the doctors preferred it to be only through computers whereas 45.2% per cent preferred consultation through smartphones and 50% of them preferred it be both ways. More than half (59.5%) of the doctors preferred the pandemic scenario and the remaining for its continued usage post-pandemic. Although India has the world's second-largest online market, a major population in India is digitally illiterate according to the Digital Foundation of India. Thus, it is important to devise telehealth technology that is simplest to use to reach also the economically backward patient communities.

Monitoring blood glucose levels is a vital indicator of diabetes mellitus management. The mainstream techniques of glucometers are invasive, painful, expensive, intermittent, and time-consuming. The ever-increasing number of global diabetic patients urges the development of alternative non-invasive glucose monitoring techniques. Recent advances in electrochemical biosensors, biomaterials, wearable sensors, biomedical signal processing, and microfabrication technologies have led to significant research and ideas in elevating the patient's life quality. This review provides up-to-date information about the available technologies and compares the advantages and limitations of invasive and non-invasive monitoring techniques. The scope of measuring glucose concentration in other bio-fluids such as interstitial fluid (ISF), tears, saliva, and sweat are also discussed. The high accuracy level of invasive methods in measuring blood glucose concentrations gives them superiority over other methods due to lower average absolute error between the detected glucose concentration and reference values. Whereas minimally invasive, and non-invasive techniques have the advantages of continuous and pain-free monitoring. Various blood glucose monitoring techniques have been evaluated based on their correlation to blood, patient-friendly, time efficiency, cost efficiency, and accuracy. Finally, this review also compares the currently available glucose monitoring devices in the market.

Thermal threshold testing is important for evaluating the thermal function of small-fibre nerve types C and A-delta. This study investigated the reliability and validity of a novel nerve testing device (NNTD) in evaluating thermal detection and thermal pain thresholds. Test-retest reliability of the NNTD and its concurrent validity compared to the current technology (Medoc TSA-2, Advanced Thermosensory Stimulator, Israel) were investigated among 10 healthy participants. Each participant was tested for the warm detection threshold (WDT), cold detection threshold (CDT), hot pain threshold (HPT) and cold pain threshold (CPT) on the medial forearm with NNTD for two trials and the Medoc TSA-2 for one trial over two consecutive days. Intraclass Correlation Coefficient values, Standard Error of Measurement and Bland Altman plots were calculated for test-retest reliability. One-way ANOVA and Bland Altman plots were calculated for validity. The test-retest reliability of the NNTD was good for CPT (ICC = 0.88), moderate for WDT (ICC = 0.545) and HPT (ICC = 0.710). The NNTD was valid for both trials of HPT and CPT and one trial for WDT compared to the Medoc TSA-2. In conclusion, the NNTD showed good to moderate reliability and was found to be valid compared to the Medoc TSA-2.

In view of the gaps between the current clinical thermometer standards and the commercially available products, in particular the introduction of forehead radiation thermometers into the market, ISO initiated a review on standard for clinical thermometers. The original clinical thermometer standard includes requirements for evaluating tympanic (ear) thermometers using identified reference blackbody sources. However, one of the challenges in reviewing the standard is to understand if the reference blackbody sources identified for ear thermometers are still usable for evaluating the laboratory accuracy of forehead thermometers. In order to answer this challenge, four brand new forehead thermometers from two different manufacturers are evaluated at NMC. Two reference blackbody sources, one JIS standard source with very small opening and one NMC source with larger opening, are used for the evaluation. In order to use JIS standard source, two types of adaptors, one supplied by one of the manufactures, one 3D printed, are evaluated. The measurement results together with their measurement uncertainties are presented and analysed. Suitable adaptors are identified and recommended.

An exploratory study was performed to determine the distribution of surgical smoke particulate matter (SSPM) and evacuation times within an AirSeal^® System and a traditional insufflation access system in various simulated surgical scenarios. Identified trends showed statistical significance when setting the AirSeal^® System to Low smoke evacuation that it reduces the percentage of particulate matter at the Access Port opening. Additionally, it was observed that when utilising a laparoscopic tool a similar trend in particle distributions were seen between either insufflation and access system at the opening of the Access Port and trocar. Evacuation times for SSPM removal within the AirSeal^® System showed an overall average to ≥95% reduction of 5.64 min within the surgical cavity, 3.69 min at the Access Port opening, and 3.61 min within the smoke evacuation line. The overall average for the traditional insufflation and access system was 9.38 min within the surgical cavity and 6.06 min at the trocar opening. Results showed that when using the traditional system compared to the AirSeal^® System, it resulted in a percent change increase in evacuation times of 66.31% within the surgical cavity and 64.23% at the trocar opening.

In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.

Understanding the relationship between mechanobiology and the biosynthetic activities of the valve interstitial cells (VICs) in health and disease under severe dynamic loading conditions is of particular interest. The purpose of this study is to further understand the mechanobiology of heart valve leaflet tissue and the VICs under impact forces. Two novel computational and experimental platforms were developed to study the effect of impact load on the VICs to monitor for apoptosis. The first objective was to design and develop an apparatus to experimentally study viability (apoptosis) of the porcine heart valve leaflet tissue VICs in the aortic position under controlled impact forces. Apoptosis was assessed based on terminal transferase dUTP nick end-labelling (TUNEL) assay. The second objective was to develop a computational platform to estimate the stress and strain fields in the vicinity of VICs when the tissue experiences impact forces. A nonlinear finite element (FE) model with an anisotropic, hyperelastic and heterogeneous material model for the matrix and cells was developed. Preliminary results confirm that interstitial cells are successfully resistant to impact loads up to 30 times more than normal physiological conditions. Additionally, the structure and composition of heart valve leaflet tissue provides a mechanical shield for VICs protecting them from excessive mechanical forces such as impact loads. Although, the entire tissue may experience excessive stresses, which may lead to structural damage, the stresses around and near VICs remain consistency low. Results of this study may be used for heart valve leaflet tissue-engineering, as well as further understanding the mechanobiology of the VICs in health and disease.

Dementia is a general term used to indicate any disorder related to human memory. The various memory-related problems severely affect the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia that exist, but the commonly seen and fatal types of dementia are Alzheimer's disease (AD) and Parkinson's disease (PD). In this paper different efficient Machine Learning Techniques are selected analysed their behaviours in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories in the ratio of 7:3 for training and testing respectively. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD with the reference of a healthy brain is done by comparing the input image with the trained samples in the PET image database. In the comparison of trained samples with the input image for the PET images, the bagged ensemble learning classifier worked better than the other classification algorithms and yielded an accuracy of 90.3%.

Autistic children have difficulty communicating wants, needs, and thoughts. In cases of low functioning autism, an individual can have serious speech and language disabilities. In response to the need of parents and therapists dealing with autistic children, social robots have been developed and incorporated into assisting children with autism. Research studies have proved the possibility of improving autistic children's lives using social robots. However, not any social robot can successfully fulfil this task, since autistic children are sensitive to certain behaviours, shapes, and colours. The proposed integrated application and humanoid robot, Otisma has been designed and developed after an intensive literature research. Otisma can be used as an educational and communicational tool with autistic children. Otisma aids educators by using applied behaviour analysis (ABA) which is a scientifically validated method that helps autistic children work through areas of severe developmental delay, like language and nonverbal communication. Different lessons can be explored using an application that is controlled by the therapist. Otisma is an efficient educational tool that facilitates the learning process to maximise social skills for children with autism. Otisma is portable, affordable, user friendly, and can be equally used by therapists in clinics or parents at home.