Open electronics for medical devices: State-of-art and unique advantages

A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Clinical Applications of Electrical Impedance Tomography in Stroke and Traumatic Brain Injury

Electrical Impedance Tomography (EIT) is a medical imaging technology which uses voltage measurements on the boundaries to reconstruct internal conductivity changes. When applied to imaging brain function, EIT is challenged by the unique geometry of the head and the high variability in the conductivities of brain tissue. Stroke and Trau-matic Brain Injury (TBI) are two of the leading causes of death and long-term disability worldwide. It has been suggested that EIT, which is already in clinical use primarily as a means of assessing lung function, could be used as a pre-hospital diagnostic tool for stroke and TBI, and for bedside monitoring for brain injury patients. The main aim of this PhD thesis is to bring the application of EIT in brain injury closer to regular clinical use. Chapter 1 introduces the concepts of EIT, stroke and TBI, and provides a comprehensive review of clinically relevant neuroimaging techniques and the current state of brain EIT. Chapter 2 presents the results of a series of lab experiments designed to investigate the characteristics and mechanisms of drift in measured boundary voltages, which is the key technical barrier to brain monitoring with EIT. Ex-periments were conducted on lab phantoms, vegetable skin, and healthy human subjects. Chapter 3 describes a feasibility study of monitoring for brain injury with EIT over several hours, using noise recorded on real healthy volunteers. This study also compares the performance of different electrode types. Chapter 4 presents a clinical pilot study performed on acute stroke patients. Multi-frequency (MF) EIT data were record-ed on patients and healthy controls to create the first of its kind clinical EIT dataset to be used as a resource for future research for the EIT community. Finally, the ability to identify stroke patients is demonstrated on the clinical EIT dataset

Remote diagnostics and monitoring using microwave technique – improving healthcare in rural areas and in exceptional situations

Interests towards wireless portable medical diagnostics and monitoring systems, which could be used outside hospital e.g. during pandemic or catastrophic situations, have increased recently. Additionally, portable monitoring solutions could partially address widely recognized challenges related to healthcare equality in rural areas. Microwave based sensing has recently been recognized as emerging technology for portable medical monitoring and diagnostics devices since they may enable development of safe, reliable, and low-cost solutions for future’s telemedicine. The aim of this paper is to present the basic idea of microwave -based medical monitoring and discuss its possibilities, advantages, and challenges. In particular, we show that microwaves could be exploited in three pre-diagnostics applications: 1) Detection of abnormalities in the brain with a helmet type of monitoring device, 2) Detection of breast cancer with a self-monitoring vest, 3) Detection of blood clots in leg with an antenna band. The technique is based on detecting differences in radio channel responses caused by the abnormalities having different dielectric properties than the surrounding tissues. Our results of realistic simulations and experimental measurements show that even small-sized abnormalities, e.g. tumors, can change channel characteristics in detectable level

Artificial intelligence and Machine Learning based Techniques in Analyzing the COVID-19 Gene Expression data: A Review

The novel Coronavirus associated with respiratory illness has become a new threat to human health as it is spreading very rapidly among the human population. Scientists and healthcare specialists throughout the world are still looking for a breakthrough technology to help combat the Covid-19 outbreak, despite the recent worldwide urgency. The use of Machine Learning (ML) and Artificial Intelligence (AI) in earlier epidemics has encouraged researchers by providing a fresh approach to combating the latest Coronavirus pandemic. This paper aims to comprehensively review the role of AI and ML for analysis of gene expressed data of COVID-1

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions