Journal of Medical Engineering and Technology最新文献

Antibiotic resistance causes a major threat to patients suffering from infectious diseases. Accurate and timely assessment of Antibiotic Susceptibility Test (AST) is of great importance to ensure adequate treatment for patients and for epidemiological monitoring. Disc Diffusion Test (DDT) is a standard and widely used method for AST. Manual interpretation of DDT results is a tedious task and susceptible to human errors. Computer vision-based automated interpretation of DDT results will speed up the process and reduces the manpower requirement. This would assist the physician to initiate the antibiotic treatment for the patients on time and results in saving the patient's life. The crucial step in automatic interpretation of DDT result is to measure and present the diameter of zone of inhibition without manual intervention. The existing methods require manual interventions at various stages during inhibition zone diameter measurement for some typical cases. This issue is addressed in the present work through maximally stable extremal regions (MSER) based algorithm. Dataset consisting of 60 agar plate images that includes different agar medium, images having different resolution and visual quality is used to validate the proposed method. Experimental results demonstrated that there is a strong correlation between standard method and the proposed method.

Total ankle arthroplasty is the gold standard surgical treatment for severe ankle arthritis and fracture. However, revision surgeries due to the in vivo failure of the ankle implant are a serious concern. Extreme bone density loss due to bone remodelling is one of the main reasons for in situ implant loosening, with aseptic loosening of the talar component being one of the primary reasons for total ankle arthroplasty revisions. This study is aimed at determining the performance and potential causes of failure of the talar component. Herein, we investigated the stress, strain, and bone density changes that take place in the talus bone during the first 6 months of bone remodelling due to the total ankle arthroplasty procedure. Computed tomography scans were used to generate the 3D geometry used in the finite element (FE) model of the Intact and implanted ankle. The Scandinavian Total Ankle Replacement (STAR™) CAD files were generated, and virtual placement within bone models was done following surgical guidelines. The dorsiflexion physiological loading condition was investigated. The cortical region of the talus bone was found to demonstrate the highest values of stress (5.02 MPa). Next, the adaptive bone remodelling theory was used to predict bone density changes over the initial 6-month post-surgery. A significant change in bone density was observed in the talus bone due to bone remodelling. The observed quantitative changes in talus bone density over 6-month period underscore potential implications for implant stability and fracture susceptibility. These findings emphasise the importance of considering such biomechanical factors in ankle implant design and clinical management.

Haemorrhage is the leading cause of battlefield deaths and second most common cause for civilian mortality worldwide. Biomaterials-based haemostatic agents are used to aid in bleeding stoppage; mesoporous bioactive glasses (MBGs) are candidates for haemostasis. Previously made Tantalum-containing MBG (Ta-MBG) powders' compositions were fabricated as electrospun fibres for haemostatic applications in the present study. The fibres were fabricated to address the challenges associated with the powder form: difficult to compress without gauze, getting washed away in profuse bleeding, generating dust in the surgical environment, and forming thick callus-difficult to remove for surgeons and painful for patients. Ta-MBGs were based on (80-x)SiO₂-15CaO-5P₂O₅-xTa₂O₅ mol% compositions with x = 0 (0Ta), 0.5 (0.5Ta), 1 (1Ta), and 5 (5Ta) mol%. The present study details the fibres' in vitro analyses, elucidating their cytotoxic effects, and haemostatic capabilities and relating these observations to fibre chemistry and previously fabricated powders of the same glasses. As expected, when Ta addition is increased at the expense of silica, a new FTIR peak (non-bridging oxygen-silicon, Si-NBO) develops and Si-O-Si peaks become wider. Compared to 0Ta and 1Ta fibres, 0.5Ta show Si-O peaks with reduced intensity. The fibres had a weaker intensity of Si-NBO peaks and release fewer ions than powders. A reduced ion profile provides fibres with a stable matrix for clot formation. The ion release profile for 1Ta and 5Ta fibres was significantly lower than 0Ta and 0.5Ta fibres. Ta-MBGs were not found to be cytotoxic to primary rat fibroblasts using a methyl thiazolyl tetrazolium (MTT) assay. Furthermore, a modified activated partial thromboplastin time assay analysing the fibrin absorbance showed that the absorption increases from physiological clotting < 0Ta < 0.5Ta < 5Ta < commercial haemostat, Surgical SNoW^TM, Ethicon, USA < 1Ta. Higher absorption signifies a stronger clot. It is concluded that Ta-MBG fibres can provide stable matrix for clot formation and 1Ta can potentially enhance clotting best among other Ta-MBGs.

Diabetes remains a critical global health concern that necessitates urgent attention. The contemporary clinical approach to closed-loop care, specifically tailored for insulin-dependent patients, aims to precisely monitor blood glucose levels while mitigating the risks of hyperglycaemia and hypoglycaemia due to erroneous insulin dosing. This study seeks to address this life-threatening issue by assessing and comparing the performance of different controllers to achieve quicker settling and convergence rates with reduced steady-state errors, particularly in scenarios involving meal interruptions. The methodology involves the detection of plasma blood glucose levels, delivery of precise insulin doses to the actuator through a control architecture, and subsequent administration of the calculated insulin dosage to patients based on the control signal. Glucose-insulin dynamics were modelled using kinetics and mass balance equations from the Bergman minimal model. The simulation results revealed that the PID controller exhibited superior performance, maintaining blood glucose concentration around the preferred threshold ∼98.8% of the time, with a standard deviation of 2.50. This was followed by RST with a success rate of 98.5% and standard deviation of 5.00, SPC with a success rate of 58% and standard deviation of 2.99, SFC with a success rate of 55% and standard deviation of 10.08, and finally LCFB with a rate of 10% and significantly higher standard deviation of 64.55.

Access to medical technologies is a critical component of universal access to care; however, the advancement of technologies for children has historically lagged behind those for adults. The small market size, anatomic and physiologic variability, and legal and ethical implications pose unique barriers to developing and commercialising paediatric biomedical innovations. These challenges are magnified in low-resource settings (LRS), which often lack appropriate regulatory oversight, support for service contracts, and supply chain capacity. The COVID-19 pandemic exposed shortcomings in the traditional industry model for medical technologies, while also catalysing open-source approaches to technology development and dissemination. Open-source pathways - where products are freely licenced to be distributed and modified - addressed key shortages in critical equipment. Relatedly, we argue that open-source approaches can accelerate paediatric global health technology development. Open-source approaches can be tailored to clinical challenges independent of economic factors, embrace low-cost manufacturing techniques, and can be highly customisable. Furthermore, diverse stakeholders, including families and patients, are empowered to participate in collaborative communities of practice. How to regulate the development, manufacture, and distribution of open-source technologies remains an ongoing area of exploration. The need for democratised innovation must be carefully balanced against the imperatives of safety and quality for paediatric-specific solutions. This can be achieved, in part, through close coordination between national regulatory agencies and decentralised networks where products can be peer-reviewed and tested. Altogether, there is significant potential for open source to advance more equitable and sustainable medical innovations for all children.

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.