IEEE Reviews in Biomedical Engineering最新文献

The advent of flexible, compact, energy-efficient, robust, and user-friendly wearables has significantly impacted the market growth, with an estimated value of 61.30 billion USD in 2022. Wearable sensors have revolutionized in-home health monitoring by warranting continuous measurements of vital parameters. Ultrasound is used to non-invasively, safely, and continuously record vital parameters. The next generation of smart ultrasonic devices for healthcare integrates microelectronics with flexible, stretchable patches and body-conformable devices. They offer not only wearability, and user comfort, but also higher tracking accuracy of immediate changes of cardiovascular parameters. Moreover, due to the fixed adhesion to the skin, errors derived from probe placement or patient movement are mitigated, even though placement at the correct anatomical location is still critical and requires a user's skill and knowledge. In this review, the steps required to bring wearable ultrasonic systems into the medical market (technologies, device development, signal-processing, in-lab validation, and, finally, clinical validation) are discussed. The next generation of vascular ultrasound and its future research directions offer many possibilities for modernizing vascular health assessment and the quality of personalized care for home and clinical monitoring.

Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other radiological modalities and improved evaluation methods predicted as important areas of future development.

Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD.

Harvesting energy from the human body is an area of growing interest. While several techniques have been explored, the focus in the field is converging on using Glucose Fuel Cells (GFCs) that use glucose oxidation reactions at an anode and oxygen reduction reactions (ORRs) at a cathode to create a voltage gradient that can be stored as power. To facilitate these reactions, catalysts are immobilized at an anode and cathode that result in electrochemistry that typically produces two electrons, a water molecule, and gluconic acid. There are two competing classes of these catalysts: enzymes, which use organic proteins, and abiotic options, which use reactive metals. Enzymatic catalysts show better specificity towards glucose, whereas abiotic options show superior operational stability. The most advanced enzymatic test showed a maximum power density of 119 μW/cm² and an efficiency loss of 4% over 15 hours of operation. The best abiotic experiment resulted in 43 μW/cm² and exhibited no signs of performance loss after 140 hours. Given the range of existing implantable devices' power budget from 10μW to 100mW and expected operational duration of 10 years or more, GFCs hold promise, but considerable advances need to be made to translate this technology to practical applications.

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Techniques to resolve images beyond the diffraction limit of light with a large field of view (FOV) are necessary to foster progress in various fields such as cell and molecular biology, biophysics, and nanotechnology, where nanoscale resolution is crucial for understanding the intricate details of large-scale molecular interactions. Although several means of achieving super-resolutions exist, they are often hindered by factors such as high costs, significant complexity, lengthy processing times, and the classical tradeoff between image resolution and FOV. Microsphere-based super-resolution imaging has emerged as a promising approach to address these limitations. In this review, we delve into the theoretical underpinnings of microsphere-based imaging and the associated photonic nanojet. This is followed by a comprehensive exploration of various microsphere-based imaging techniques, encompassing static imaging, mechanical scanning, optical scanning, and acoustofluidic scanning methodologies. This review concludes with a forward-looking perspective on the potential applications and future scientific directions of this innovative technology.

The shortened radio frequency wavelength in high field MRI makes it challenging to create a uniform excitation pattern over a large field of view, or to achieve satisfactory transmission efficiency at a local area. Transmit arrays are one tool that can be used to create a desired excitation pattern. To be effective, it is important to be able to control the current amplitude and phase at the array elements. The control of the current may get complicated by the coil coupling in many applications. Various methods have been proposed to achieve current control, either in the presence of coupling, or by effectively decouple the array elements. These methods are applied in different subsystems in the RF transmission chain: coil; coil-amplifier interface; amplifier, etc. In this review paper, we provide an overview of the various approaches and aspects of transmit current control and decoupling.