Journal of Medical Engineering and Technology最新文献

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric, termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seventeen ischaemic stroke patients wore the device for nine hours and reported feedback at 1, 3, 6 and 9 h regarding user experience, comfort, and satisfaction (acceptability). Safety was assessed as the number of adverse events reported. Feasibility was assessed as the percentage of uninterrupted EEG/ECG data recorded (data capture efficiency). No adverse events were reported, only minor incidences of discomfort. Overall device comfort (mean ± 1 standard deviation (SD) (range)) (92.5% ± 10.3% (57.0-100%)) and data capture efficiency (mean ± 1 SD (range)) (95.8% ± 6.8% (54.8-100%)) were very high with relatively low variance. The device didn't restrict participants from receiving clinical care and rarely (n = 6) restricted participants from undertaking routine tasks. This study provides a promising evidence base for the deployment of the device in a clinical setting. If clinically validated, EBPi may be able to detect CBF changes to monitor early neurological deterioration and treatment outcomes, thus filling an important gap in current monitoring options.TRIAL REGISTRATION: The study was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622000112763).

An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation from ions, which raises the possibility of malignancy, particularly when the imaging procedure is done. Access to expensive-quality CT scans and the related sophisticated analytic tools might be restricted in environments with fewer resources due to their high cost and limited availability. It will need an array of creative technological innovations to overcome such weaknesses. This paper aims to design a heuristic and deep learning-aided lung cancer classification using CT images. The collected images are undergone for segmentation, which is performed by Shuffling Atrous Convolutional (SAC) based ResUnet++ (SACRUnet++). Finally, the lung cancer classification is performed by the Adaptive Residual Attention Network (ARAN) by inputting the segmented images. Here the parameters of ARAN are optimally tuned using the Improved Garter Snake Optimization Algorithm (IGSOA). The developed lung cancer classification performance is compared to conventional lung cancer classification models and it showed high accuracy.

This paper delves into the diverse applications and transformative impact of additive manufacturing (AM) in biomedical engineering. A detailed analysis of various AM technologies showcases their distinct capabilities and specific applications within the medical field. Special emphasis is placed on bioprinting of organs and tissues, a revolutionary area where AM has the potential to revolutionize organ transplantation and regenerative medicine by fabricating functional tissues and organs. The review further explores the customization of implants and prosthetics, demonstrating how tailored medical devices enhance patient comfort and performance. Additionally, the utility of AM in surgical planning is examined, highlighting how printed models contribute to increased surgical precision, reduced operating times, and minimized complications. The discussion extends to the 3D printing of surgical instruments, showcasing how these bespoke tools can improve surgical outcomes. Moreover, the integration of AM in drug delivery systems, including the development of innovative drug-loaded implants, underscores its potential to enhance therapeutic efficacy and reduce side effects. It also addresses personalized prosthetic implants, regulatory frameworks, biocompatibility concerns, and the future potential of AM in global health and sustainable practices.

Prolonged air leakage (AL) following pulmonary resections leads to prolonged hospital stay and post-operative complications. Intra- and postoperative quantification of AL might be useful for improving treatment decisions, but these measurements have not been characterised. AL calculations based on inspiratory and expiratory tidal volumes were investigated in an Intensive Care Unit mechanical ventilator circuit (Servo-I). AL was also measured by a digital chest drainage system. This study shows that AL measurements increase in accuracy when corrected for baseline deviations (R: 0.904 > 0.997, p < 0.001). Bland-Altman analysis revealed a funnel-shape, indicative of a detection threshhold. Corrected measurements were most accurate when averaged over five breaths and AL was >500 mL/min, with an estimated mean systemic bias of 7.4% (95%-limits of agreement [LoA]: 1.1%-13.7%) at 500 mL/min air leak. Breath-by-breath analysis showed most accurate results at AL >20 mL/breath (R: 0.989-0.991, p < 0.001) at tidal volumes between 350-600 mL. The digital drain had a mean systemic bias of -11.1% (95%-LoA: -18.9% to -3.3%) with homogenous scatter in Bland-Altman analysis and a strong correlation to the control measurement over a large range (0-2000mL/min, R: 0.999, p < 0.001). This study indicates that the Servo-I can be used for air leak quantification in clinically relevant ranges (>500 mL/min), but is unsuited for small leak detection due to a detection threshold. Researchers and clinicians should be aware of varying accuracy and interoperability characteristics between AL measurement devices.

In recent years, transmitting medical data has been a regular process. Although strong, safe, and dependable encryption techniques are necessary for medical data, cryptography is largely a computational process. The research presents a selective encryption approach for the transfer of sensitive data. This study proposes a novel technique for selecting the optimal keys to offer more security to medical data. Initially, the medical data is encrypted using the hybrid AES-DES technique. To make an efficient encryption method, the most optimal keys are selected utilising an improved Cheetah optimisation algorithm (ICO). Finally, the keys are optimised, and the input medical data is safely kept in the cloud system according to the established model. As a result, the proposed approach utilises the Python tool to evaluate the results. The simulation results show that the proposed method outperforms others in terms of encryption time 96 s, decryption time 92 s, memory usage (16), and latency (0.006).

This paper aims to investigate the impact of conventional rehabilitation training and neuromuscular electrical stimulation (NMES) on the recovery of motor abilities in patients following ligament injury reconstruction. Forty postoperative patients who underwent surgery for anterior cruciate ligament reconstruction (ACLR) were randomly allocated to either the conventional rehabilitation group or the NMES group. The NMES group received NMES treatment in addition to the conventional rehabilitation program starting from eight weeks postoperatively. Various parameters, including knee joint function, stability, and balance, were compared between the two groups at eight weeks and 12 weeks postoperatively. Compared to the data at eight weeks postoperatively, both groups exhibited significant improvements in all measured indicators at 12 weeks postoperatively (p < 0.05). In the 12^th week after the surgery, the NMES group demonstrated a Lysholm score of 93.18 ± 3.67 points, an IKDC score of 84.65 ± 2.33 points, a KT-2000 measurement of 0.88 ± 0.45 mm, a thigh circumference difference of -1.33 ± 0.55 cm, a knee flexion angle of 130.12 ± 4.21°, a single-leg standing time of 60.12 ± 9.33 s, a YBT score of 70.26 ± 2.68 points, and a Bulgarian split squat 1RM size of 58.07 ± 6.85 kg; all of these results were significantly superior to those observed in the conventional group (p < 0.05). NMES significantly enhances the recovery of athletic ability in patients following postoperative ACLR and can be effectively applied in clinical practice.

Acute limb ischaemia (ALI) is an emergent clinical condition that strains pre-hospital resources and impacts healthcare costs and patient quality of life. Hypothermia has long been used in clinical and research settings to mitigate ischaemic damage in tissues, but prompt reperfusion is needed to prevent loss of limb or function from ALI. To address the unmet need for pre-hospital intervention of threatened limbs awaiting definitive specialty care, we have focused on controlled application of hypothermia. Over years of animal experiments, phantom limb creation, and materials selection, we conceptualised and created a portable limb-cooling device that can be used alone or combined with a traditional tourniquet or resuscitative endovascular balloon occlusion of the aorta. Here, we describe our process of building and testing the device, from computer simulation through animal-limb metabolic studies, to prototype testing.

Wearable computers can be used in different domains including healthcare. However, due to suffering from challenges such as faults their applications may be limited in real practice. So, in designing wearable devices, designer must take into account fault tolerance techniques. This study aims to investigate the challenging issues of fault tolerance in wearable computing. For this purpose, different aspects of fault tolerance in wearable computing namely hardware, software, energy, and communication are studied; and state of the art research regarding each category is analysed. In this analysis, the performed works using the fault tolerance techniques are included in the form of 25 components and referred to as "fault tolerance plan". Using this fault tolerance plan and the appropriate profile, the fault tolerance of any wearable system can be evaluated. In this article, fault tolerances of several of the most prominent works conducted in the field of wearable computing were evaluated. The obtained results, with the medical profile, showed that only one wearable system had a fault tolerance of 91%, with the other systems having a fault tolerance of 24% or less. Also, the results obtained from evaluating these works, with the military profile, showed that only one wearable system had a fault tolerance of 76%, with the other systems having a fault tolerance of 19% or less. These mean that few studies have been conducted on the fault tolerance of wearable computing.

Wound healing requires a substantial amount of moisture for faster recovery. Completely hydrophobic or hydrophilic biomaterials are not suitable to be applied for cell growth in wounded areas. The study aimed to prepare a nanofibrous scaffold from the blend of a solution of hydrophobic PLA and a solution of hydrophilic gelatine. The stability of the blend was achieved using a surfactant and an electrospun nanofibrous scaffold was made out of the solution. The optimum composition of gelatine and PLA to make a scaffold of uniform fibre diameter was achieved with the help of conductivity, viscosity and FESEM analysis. The optimum scaffold was characterised by TGA, DSC and XRD analysis. The water contact angle of the optimum sample was observed at 27°. The blended scaffold was found non-toxic to cells and showed a 30% faster healing of wounds in the rat model test compared to the healing rate of the PLA scaffold or the gelatine scaffold alone. The histological assay also supported the blend scaffold as an encouraging material for tissue regeneration.