IEEE Reviews in Biomedical Engineering最新文献

Focused ultrasound combined with intravenously infused microbubbles has been shown to effectively enhance the permeability of the blood-brain barrier, facilitating drug delivery to the brain. A wide range of technical parameters has been evaluated through preclinical studies and clinical trials. Generally, a low frequency between 200 and 300 kHz is preferred for the transcranial approach, while 1 MHz is used in implantable devices. Standard parameters include a burst length of 5 to 10 ms, a pulse repetition frequency of 0.2 to 10 Hz, and sonication durations of 90 to 180 seconds. A pressure magnitude around 0.46 mechanical index appears to be near the threshold for BBB permeability enhancement at standard microbubble dosage without causing hemorrhage. Various microbubble and nanobubble types have been tested at different doses, which in principle can be normalized by gas volume. Control methods that use harmonic emmisions for power feedback have been proposed to enhance consistency and account for patient variability, and these methods are currently being tested in several clinical trials.

Neurorehabilitation conventionally relies on the interaction between a patient and a physical therapist. Robotic systems can improve and enrich the physical feedback provided to patients after neurological injury, but they under-utilize the adaptability and clinical expertise of trained therapists. In this position paper, we advocate for a novel approach that integrates the therapist's clinical expertise and nuanced decision-making with the strength, accuracy, and repeatability of robotics: Robot-mediated physical Human-Human Interaction. This framework, which enables two individuals to physically interact through robotic devices, has been studied across diverse research groups and has recently emerged as a promising link between conventional manual therapy and rehabilitation robotics, harmonizing the strengths of both approaches. Although current findings are largely based on pilot studies and conceptual frameworks, integrating therapists' expertise with the functionalities offered by robotic systems represents a promising direction for improving rehabilitation outcomes. This paper presents the rationale of a multidisciplinary team-including engineers, doctors, and physical therapists-for conducting research that utilizes: a unified taxonomy to describe robot-mediated rehabilitation, a framework of interaction based on social psychology, and a technological approach that makes robotic systems seamless facilitators of natural human-human interaction.

Transcranial focused ultrasound (tFUS) is an emerging neuromodulation and therapeutic technology offering noninvasive, submillimeter precision for targeting deep brain structures. Unlike transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES), which are limited by depth-focality tradeoffs, or deep brain stimulation (DBS), which is invasive and costly, tFUS enables precise modulation with minimal risk. Its applications include ablation for movement and psychiatric disorders, blood-brain barrier opening (BBBO) for drug delivery in neuro-oncology and neurodegeneration, and neuromodulation for circuit-based interventions in addiction, mood/anxiety disorders, and chronic pain. Advances in phased-array transducers, holographic focusing, and real-time imaging continue to refine its accuracy and safety. Ongoing research explores closed-loop systems and wearable devices to expand clinical accessibility. This review outlines the physics, current applications, and future directions of tFUS, positioning it as a transformative tool in personalized neuromodulation and neurotherapeutics.

Optogenetics has emerged as a pivotal tool in neuroscience, enabling intricate modulation of targeted neurons within the nervous system. Despite its transformative potential, achieving high spatiotemporal resolution in neuromodulation remains a significant challenge, particularly in free-behaving animals. This review aims to highlight recent advances in optogenetic systems for neuromodulation, focusing on the efforts to achieve superior precision in spatiotemporal control. We provide a comprehensive overview of the breakthroughs in optogenetic tools that offer ultrafast responsiveness, strategies for targeted tissue- and cell-specific optogene delivery, and methods for precise optical stimulation with minimal impact on the behavior of subjects. Additionally, we review the applications of optogenetics in neurological diseases, emphasizing its potential to advance therapeutic interventions. These innovations are poised to propel optogenetics into a new era, accelerating its clinical translation for precision neuromodulation and treatment of neurological disorders.

Human neuroscience is undergoing a paradigm shift from traditional lab settings to natural environments. Functional Near Infrared Spectroscopy (fNIRS) and its variant, High-Density Diffuse Optical Tomography (HD-DOT) are rapidly evolving techniques that are increasingly adopted across disciplines. The high ease of use of advanced systems can enable continuous brain monitoring and thus the acquisition of large amounts of data. Integrating these data with modern deep learning (DL) promises to offer robust and generalizable solutions to ongoing challenges in fNIRS-related domains. As DL is a rather new field in fNIRS, we conduct a method-focused review, discussing 100 papers in the context of architectures, applications, and learning strategies. Based on the limitations in literature and the research gap between fNIRS and other domains, we conduct a tutorial study with guidelines from the wider DL field. We focus on: straightforward pre-processing pipelines; the trade-off between available data and model complexity of different architectures, including transformers; the generalizability of models for unseen data; and explainability. Finally, we provide a problem-focused discussion, gathering essential problems in the community, and introduce advanced DL solutions. This review serves as a strategic guide for advancing the current methodology for DL approaches in the fNIRS field.

Content generation modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and task-oriented generation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, molecular profile-morphology generation, and other specialized generation applications. By analyzing over 150 representative studies, we trace the evolution of content generation architectures-from early generative adversarial networks to recent advances in diffusion models and generative vision-language models. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing integrated and clinically deployable generation systems. This work aims to provide a foundational reference for researchers and practitioners developing content generation models in computational pathology.

The Robotic Ultrasound System (RUSS) has the potential to transform medical imaging by addressing limitations such as operator dependency, diagnostic variability, and reproducibility in traditional ultrasound (US) examination. Despite rapid technological advancements, a substantial gap remains between RUSS research progress and clinical adoption. This review examined the clinical roles and engineering advances of RUSS, identifying key barriers to translation. Clinically, it evaluated the current applications of RUSS in supporting US procedures, while from an engineering standpoint, it summarized recent innovations and remaining technical challenges. This review examined the current state-of-the-art RUSS technologies, categorizing them based on diverse organ-specific applications while also analyzing their core functional capabilities. This review revealed a focus disparity: while abdominal US is the most commonly used in clinical practice, vascular-targeted RUSS dominates current research. It also highlighted a misalignment between research priorities and actual clinical tasks. Current studies predominantly focused on autonomous scanning and imaging, with limited attention to downstream tasks such as disease diagnosis and analysis. Building on these observations, it identified critical challenges and future trends in RUSS development. This work provides a foundation for future research, fostering collaboration between clinicians and engineers to accelerate the translation of next-generation RUSS from bench to bedside.

Backed by a century of research and development, Hill-type models of skeletal muscle, often including a muscle-tendon complex and neuromechanical interface, are widely used for countless applications. Lacking recent comprehensive reviews, the field of Hill-type modeling is, however, dense and hard-to-explore, with detrimental consequences on innovation. Here we present the first systematic review of Hill-type muscle modeling. It aims to clarify the literature by detailing its contents and critically discussing the state-of-the-art by identifying the latest advances, current gaps, and potential future directions in Hill-type modeling. For this purpose, fifty-eight criteria-abiding Hill-type models were assessed according to a completeness evaluation, which identified the modelled muscle properties, and a modeling evaluation, which considered the level of validation and reusability of the models, as well as their modeling strategy and calibration. It is concluded that most models (1) do not significantly advance beyond historical foundational standards, (2) neglect the importance of parameter identification, (3) lack robust validation, and (4) are not reusable in other studies. Besides providing a convenient tool supported by extensive supplementary materials for navigating the literature, the results of this review highlight the need for global recommendations in Hill-type modeling to optimize inter-study consistency, knowledge transfer, and model reusability.

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics-related tasks. Despite the transformative potential of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.