IEEE Reviews in Biomedical Engineering最新文献_第2页

Decoding Spikes From Multiunit Data 从多单元数据解码尖峰。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2026-01-22 DOI: 10.1109/RBME.2025.3647848

Dario Farina;Tianyi Yu

{"title":"Decoding Spikes From Multiunit Data","authors":"Dario Farina;Tianyi Yu","doi":"10.1109/RBME.2025.3647848","DOIUrl":"10.1109/RBME.2025.3647848","url":null,"abstract":"Communication and control in biological systems is mediated by the timing of discharges –<italic>spikes– from excitable cells such as neurons and muscle fibers. Each spike is associated to a characteristic waveform that can be captured by sensors. The waveform's characteristics depend on the cell's biophysical properties and the recording modality. Depending on the technique, e.g., electrical recordings with electrodes, optical imaging, ultrasound, the observed signals are mixtures of waveforms emitted from active cells/sources (<italic>multiunit data/signals). Recovering the timing and identity of these sources (multiunit or spike decoding) is central to neuroscience, clinical diagnostics, and neural interfacing, yet it remains challenging due to waveform superposition, non-stationarity, limited training labels, and the computational demands of high-density recordings. This review provides a unified methodological perspective on spike decoding by formalizing the problem as a sparse source separation task under a convolutive mixing model. Rather than organizing the literature by application domain, we group and critically compare methods by their underlying principles: classical spike sorting, Bayesian and probabilistic inference, blind source separation, and data-driven approaches, including deep learning and hybrid schemes. For each class of methods, we present the core mathematical formulation and algorithmic strategies and discuss assumptions and limitations. Our synthesis highlights parallels in signal processing across physical recording modalities and clarifies when and why particular approaches succeed or fail. By bridging previously compartmentalized literature, this survey aims to accelerate crosspollination of ideas between application areas and to provide a roadmap for selecting, adapting, and advancing decoding methods across diverse multiunit recording modalities.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"3-23"},"PeriodicalIF":12.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11361153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression 神经退行性疾病的网络模型：连接神经元动力学和疾病进展。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2026-01-16 DOI: 10.1109/RBME.2025.3643310

Christoffer G. Alexandersen;Georgia S. Brennan;Julia K. Brynildsen;Michael X. Henderson;Yasser Iturria-Medina;Dani S. Bassett

{"title":"Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression","authors":"Christoffer G. Alexandersen;Georgia S. Brennan;Julia K. Brynildsen;Michael X. Henderson;Yasser Iturria-Medina;Dani S. Bassett","doi":"10.1109/RBME.2025.3643310","DOIUrl":"10.1109/RBME.2025.3643310","url":null,"abstract":"Neurodegenerative diseases are characterized by the accumulation of misfolded proteins and widespread disruptions in brain function. Computational modeling has advanced our understanding of these processes, but efforts have traditionally focused on either neuronal dynamics or the biological processes underlying disease. One class of models uses neural mass and whole-brain frameworks to simulate changes in oscillations, connectivity, and network stability. A second class focuses on biological processes underlying disease progression, particularly prion-like propagation through the connectome, glial responses and vascular mechanisms. Each modeling tradition has provided important insights, but experimental evidence shows these processes are interconnected: neuronal activity modulates protein release and clearance, while pathological burden disrupts neuronal function. Modeling these domains in isolation limits our understanding, although recent studies have begun to bridge the two by coupling neuronal and pathological processes. To determine where and why disease emerges, how it spreads, and how it might be altered, mathematical models that capture feedback between neuronal dynamics and disease biology are needed. This review surveys the two modeling approaches and highlights efforts to unify them, emphasizing that linking neuronal activity and disease progression is key to identifying strategies that slow, halt, or reverse degeneration and restore neural function.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"140-158"},"PeriodicalIF":12.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical Techniques to Assess Cutaneous Microvascular Function in Cardiovascular Disease. 评价心血管疾病患者皮肤微血管功能的光学技术。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2026-01-12 DOI: 10.1109/RBME.2025.3644411

Inka Mustajoki, Julien Riancho, Tuukka Panula, Jukka-Pekka Sirkia, Jorge Herranz Olazabal, Smriti Badhwar, Maria Kjellman, Katri Karhinoja, Maria Maia, Sam Riahi, Yannis Papadopoulos, Evelien Hermeling, Rosa-Maria Bruno, Matti Kaisti

{"title":"Optical Techniques to Assess Cutaneous Microvascular Function in Cardiovascular Disease.","authors":"Inka Mustajoki, Julien Riancho, Tuukka Panula, Jukka-Pekka Sirkia, Jorge Herranz Olazabal, Smriti Badhwar, Maria Kjellman, Katri Karhinoja, Maria Maia, Sam Riahi, Yannis Papadopoulos, Evelien Hermeling, Rosa-Maria Bruno, Matti Kaisti","doi":"10.1109/RBME.2025.3644411","DOIUrl":"https://doi.org/10.1109/RBME.2025.3644411","url":null,"abstract":"Microcirculation is essential for maintaining tissue health and overall physiological function. Over the past few decades, various optical techniques have been developed to measure, visualize, and assess microvasculature. The skin has easily an accessible vascular bed allowing for noninvasive evaluation of microvascular function. Alterations in cutaneous microcirculation have been linked to dysfunctions in other target organs and vascular regions reinforcing the idea that cutaneous microcirculation can provide insights into systemic vascular conditions. Currently, there is no unified review focusing specifically on microcirculation-related optical techniques nor comprehensive analyses connecting these technological innovations to clinical evidence. This review aims to bridge that gap by systematically examining the wide spectrum of optical technologies used in assessing cutaneous microvascular function. We review techniques based on non-coherent light including oximetry, photoplethysmography, and microscopic methods and coherent light-based techniques, including speckle contrast imaging, diffuse correlation spectroscopy, photoacousting imaging, laser Doppler flowmetry and self-mixing interferometry. We emphasize cardiovascular research and evaluate the clinical relevance and technical maturity of the techniques. Additionally, brief explanation of skin structure and skin microvasculature while explaining light skin interaction is discussed. Lastly, we discuss these findings on wider context by including discussions and advancements in multimodal monitoring and machine learning.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Establishment of High-Precision Ultrasound Diagnosis Methods Based on the Introduction of Deep Learning. 基于引入深度学习的高精度超声诊断方法的建立。

IF 16 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-31 DOI: 10.1109/RBME.2025.3645229

Masaaki Komatsu, Reina Komatsu, Akira Sakai, Suguru Yasutomi, Naoaki Harada, Rina Aoyama, Naoki Teraya, Katsuji Takeda, Takashi Natsume, Tomonori Taniguchi, Kazuki Iwamoto, Ryu Matsuoka, Akihiko Sekizawa, Ryuji Hamamoto

{"title":"Establishment of High-Precision Ultrasound Diagnosis Methods Based on the Introduction of Deep Learning.","authors":"Masaaki Komatsu, Reina Komatsu, Akira Sakai, Suguru Yasutomi, Naoaki Harada, Rina Aoyama, Naoki Teraya, Katsuji Takeda, Takashi Natsume, Tomonori Taniguchi, Kazuki Iwamoto, Ryu Matsuoka, Akihiko Sekizawa, Ryuji Hamamoto","doi":"10.1109/RBME.2025.3645229","DOIUrl":"10.1109/RBME.2025.3645229","url":null,"abstract":"Ultrasound imaging is widely used owing to its affordability, radiation-free, and non-invasive advantages. However, limitations stemming from operator dependence and artifacts have been noted. To address these issues, deep learning (DL) is increasingly being introduced. In oncology and cardiology, DL-equipped devices are transitioning to clinical use following approval. Nevertheless, DL faces challenges such as generalization, safety, and operational burden, making strategic implementation essential to maximize patient benefit. Existing reviews often list individual technologies but lack evaluation frameworks tailored to clinical implementation. Therefore, this review (i) organizes and formalizes limitations specific to ultrasound diagnosis, (ii) explains the latest DL methods addressing these limitations in terms of principles, implementation, and evaluation metrics, and (iii) examines recent clinical applications, including approved devices, supported by evidence, demonstrating that DL possesses substantial utility beyond the research stage for improving clinical workflows. It also critically evaluates remaining challenges, presents evaluation criteria to aid implementation, and identifies future research challenges.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":16.0,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assistive Trajectory Planning for Lower Limb Exoskeletons: Strategies From Laboratory-Optimized Gait to Environmentally-Adaptive Locomotion Through Multimodal Parameter Awareness 下肢外骨骼的辅助轨迹规划：通过多模态参数感知从实验室优化步态到环境适应性运动的策略。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-30 DOI: 10.1109/RBME.2025.3646165

Qihan Ye;Xingbang Yang;Ruoqi Zhao;Yuanlong Ji;Xinyuan Cai;Quan Zheng;Yubo Fan

{"title":"Assistive Trajectory Planning for Lower Limb Exoskeletons: Strategies From Laboratory-Optimized Gait to Environmentally-Adaptive Locomotion Through Multimodal Parameter Awareness","authors":"Qihan Ye;Xingbang Yang;Ruoqi Zhao;Yuanlong Ji;Xinyuan Cai;Quan Zheng;Yubo Fan","doi":"10.1109/RBME.2025.3646165","DOIUrl":"10.1109/RBME.2025.3646165","url":null,"abstract":"Lower limb assistive exoskeletons (LLEs) show great potential to reduce metabolic cost, improve walking performance, and correct abnormal gait patterns. Among their core control architectures, assistive trajectory planning is key to determining system responsiveness and effectiveness under varying locomotion conditions. Many trajectory planning methods were proposed in either laboratory or unstructured real-world environments. To clarify the scope and challenges of existing research, this review categorizes trajectory planning strategies into two major types based on application scenarios: (1) strategies for laboratory settings with controllable disturbances, which usually involve optimal control for gait under stable or controllable conditions; and (2) strategies for real-world environments characterized by varying terrain, individual differences, and gait fluctuations, which usually involve adaptive control for gait under diverse or unstructured conditions. Given the foundational role of gait phase detection in trajectory planning, this review also systematically examines mainstream algorithms for gait phase recognition and estimation. Finally, the paper analyzes the limitations of existing methods and discusses the potential of advanced algorithms, intelligent multimodal sensing systems, novel sensing technologies, and embedded deployment to enhance the performance of exoskeleton assistive trajectory planning.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"41-64"},"PeriodicalIF":12.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145865811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Models. 脑-心相互作用建模：机械动力学模型综述。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-23 DOI: 10.1109/RBME.2025.3641959

Sara Nour Sadoun, Arnaud Boutin, Francois Cottin, Taous-Meriem Laleg-Kirati

{"title":"Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Models.","authors":"Sara Nour Sadoun, Arnaud Boutin, Francois Cottin, Taous-Meriem Laleg-Kirati","doi":"10.1109/RBME.2025.3641959","DOIUrl":"https://doi.org/10.1109/RBME.2025.3641959","url":null,"abstract":"Brain-heart interaction (BHI) is fundamental to autonomic regulation and also shapes perceptual salience, attentional control, decision-making under load, and affective reactivity. Beyond these functions, BHI has been consistently implicated in clinical studies in cardiovascular, neurological, and psychiatric conditions. This reality makes the investigation of bidirectional BHI mechanisms-and the derivation of interpretable biomarkers- - indispensable for cardiovascular, physiological, and neuroscientific research that treats the body as an interoceptive network of interacting organs rather than isolated systems. The growing interest in this perspective has generated a broad spectrum of frameworks, from signal-processing pipelines and computational models to dynamical systems. Building on previous surveys that have thoroughly mapped the field and deepened our understanding, this review offers a complementary perspective centered on mechanistic, physiology-inspired models of dynamical systems. For each model, we identify the physiological subsystem described, clarify core assumptions, and assess strengths and limitations. We then outline the technical perspectives necessary to realize the full potential of these approaches - especially for inferring latent interoceptive quantities that govern directional BHI but are not directly observable, and for integrating explicit brain modeling into these frameworks to better capture the neural mechanisms driving autonomic and cardiovascular dynamics. Mechanistic dynamical modeling has, over decades, deepened our understanding of physiology and pathology and informed the mapping and treatment of diverse conditions. Our objective is to provide a comprehensive account of state-of-the-art dynamical models, delineate methodological directions, and highlight application areas where such models can yield explanatory insight, reliable prediction, and actionable clinical targets.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emerging Noninvasive Approaches for the Suppression of Pathological Tremor 新兴的无创方法抑制病理性震颤。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-16 DOI: 10.1109/RBME.2025.3639754

Cristina Montero-Pardo;Eduardo Rocon;Strahinja Dosen;Jakob Lund Dideriksen;Álvaro Gutiérrez;Javier Ricardo Pérez-Sánchez;Elisa Luque-Buzo;Elan D. Louis;Francisco Grandas;Filipe Oliveira-Barroso

{"title":"Emerging Noninvasive Approaches for the Suppression of Pathological Tremor","authors":"Cristina Montero-Pardo;Eduardo Rocon;Strahinja Dosen;Jakob Lund Dideriksen;Álvaro Gutiérrez;Javier Ricardo Pérez-Sánchez;Elisa Luque-Buzo;Elan D. Louis;Francisco Grandas;Filipe Oliveira-Barroso","doi":"10.1109/RBME.2025.3639754","DOIUrl":"10.1109/RBME.2025.3639754","url":null,"abstract":"Pathological tremor affects over 40 million people worldwide, significantly impairing daily activities and quality of life. Pharmacological treatments show limited efficacy, with up to 30% discontinuation rates, while surgical interventions like deep brain stimulation achieve significant tremor reduction but are often unsuitable due to age, comorbidities, or personal preference. Recently, the need for safe and effective alternatives has led to the development of innovative, noninvasive, and patient-friendly technologies for tremor management. This clinical application review analyzed 134 studies (1969-2025), categorizing them into three major modalities, focusing on their underlying neurophysiological mechanisms, with a special emphasis on the clinical perspective. The three considered modalities were force-controlling (orthoses and functional electrical stimulation), central neuromodulation (transcranial magnetic stimulation, transcranial electrical stimulation, low-intensity focused ultrasound, and transcutaneous spinal cord stimulation), and peripheral neuromodulation (afferent stimulation and vibration). Force-controlling strategies showed promising acute effects, though clinical translation remains limited by poor wearability and the development of muscle fatigue. Central neuromodulation produced moderate effects, while peripheral neuromodulation has gained clinical traction, with several devices now being commercially available. However, heterogeneity in study design, patient populations, and technology maturity remain the main obstacles for the direct comparison of techniques. Future research should prioritize larger multicenter trials, standardized outcome measures, and accessibility considerations to enable personalized, evidence-based treatment selection for diverse tremor populations.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"65-85"},"PeriodicalIF":12.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Readout Techniques and Offset Compensation Strategies for Biomedical Resistive MEMS Sensors: A Comprehensive Review. 生物医学电阻式MEMS传感器的读出技术和补偿策略综述。

IF 16 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-16 DOI: 10.1109/RBME.2025.3639404

Reza Bostani, Esmaeil Ranjbar Koleibi, Gabriel Gagnon-Turcotte, Rejean Fontaine, Sharmistha Bhadra, Benoit Gosselin

{"title":"Readout Techniques and Offset Compensation Strategies for Biomedical Resistive MEMS Sensors: A Comprehensive Review.","authors":"Reza Bostani, Esmaeil Ranjbar Koleibi, Gabriel Gagnon-Turcotte, Rejean Fontaine, Sharmistha Bhadra, Benoit Gosselin","doi":"10.1109/RBME.2025.3639404","DOIUrl":"10.1109/RBME.2025.3639404","url":null,"abstract":"Resistive MEMS sensors have become increasingly significant in biomedical and bioenvironmental monitoring due to their compact dimensions, low energy demand, and high sensitivity. Despite structural simplicity and integration benefits, these sensors face performance constraints arising from intrinsic nonidealities such as nonlinearity, thermal drift, parasitic interactions, and process mismatches. These limitations intensify at micro and nanoscale dimensions and generate substantial DC offset in the output. This review presents a systematic analysis of resistive sensor architectures, including single resistor, half bridge, and full bridge configurations, and evaluates their susceptibility to distortion and noise through analytical modeling. Comparative assessment reveals tradeoffs in sensitivity, linearity, noise resilience, and thermal stability. The paper also examines advanced readout methodologies designed for precision measurement, low power operation, and compact integration, including voltage to voltage, voltage to frequency, resistance to digital, and RC delay based interfaces. Particular emphasis is placed on DC offset compensation strategies that address sensor nonidealities, such as resistive, current driven, and capacitive DAC techniques, implemented across different stages of the signal chain. These approaches are critically appraised for their effectiveness in extending dynamic range, reducing energy consumption, and preserving signal fidelity in implantable and wearable platforms. The survey synthesizes recent designs and proposes a classification framework to guide the selection of interface and compensation strategies designed to sensor topology and application constraints. By integrating theoretical insights with practical design considerations, this work provides a comprehensive reference for developing robust, precise, and energy efficient resistive sensor interfaces.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":16.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions 在机器人辅助血管内手术中推进具体智能：人工智能解决方案的系统回顾。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-16 DOI: 10.1109/RBME.2025.3641383

Tianliang Yao;Bo Lu;Markus Kowarschik;Yixuan Yuan;Hubin Zhao;Sebastien Ourselin;Kaspar Althoefer;Junbo Ge;Peng Qi

{"title":"Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions","authors":"Tianliang Yao;Bo Lu;Markus Kowarschik;Yixuan Yuan;Hubin Zhao;Sebastien Ourselin;Kaspar Althoefer;Junbo Ge;Peng Qi","doi":"10.1109/RBME.2025.3641383","DOIUrl":"10.1109/RBME.2025.3641383","url":null,"abstract":"Endovascular procedures have revolutionized vascular disease treatment, yet their manual execution is challenged by the demands for high precision, operator fatigue, and radiation exposure. Robotic systems have emerged as transformative solutions to mitigate these inherent limitations. A crucial moment has arrived, where a confluence of pressing clinical needs and breakthroughs in AI creates an opportunity for a paradigm shift toward Embodied Intelligence (EI), enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, leveraging advanced computer vision, medical image analysis, and machine learning, drive this evolution by enabling real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further improve navigation strategies and replicate expert techniques. This review systematically analyzes the integration of EI into endovascular robotics, identifying challenges such as the heterogeneity in validation standards and the gap between human mimicry and machine-native capabilities. Based on this analysis, a conceptual roadmap is proposed that reframes the ultimate objective away from systems that supplant clinical decision-making. This vision of augmented intelligence, where the clinician's role evolves into that of a high-level supervisor, provides a principled foundation for the future of the field.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"248-266"},"PeriodicalIF":12.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems 聚焦FHIR：实现智能医疗保健系统的生物医学数据协调。

IF 12 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-12-10 DOI: 10.1109/RBME.2025.3632213

Andrew Hornback;Benoit Marteau;Shaun Q. Y. Tan;Kyungbeom Kim;Oankar Patil;Joshua Traynelis;Yuanda Zhu;Felipe Giuste;May D. Wang

{"title":"FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems","authors":"Andrew Hornback;Benoit Marteau;Shaun Q. Y. Tan;Kyungbeom Kim;Oankar Patil;Joshua Traynelis;Yuanda Zhu;Felipe Giuste;May D. Wang","doi":"10.1109/RBME.2025.3632213","DOIUrl":"10.1109/RBME.2025.3632213","url":null,"abstract":"Fast Healthcare Interoperability Resources (FHIR), developed by Health Level Seven International (HL7), has emerged as the leading healthcare data standard to address persistent barriers in interoperability, fragmented exchange, and inconsistent data harmonization. As health systems worldwide undergo digital transformation, FHIR offers a flexible framework for integrating electronic health records, analytics platforms, and decision-support tools. Its growth has been accelerated by policy mandates such as the 21st Century Cures Act, as well as the availability of application programming interfaces (APIs), software development kits (SDKs), and web standards. Globally, FHIR has been adopted or piloted by national health systems in the United States, United Kingdom, Canada, and Australia, and incorporated into World Health Organization data initiatives, underscoring its role in global digital health strategy. Documented outcomes of this review include comprehensive mapping of FHIR applications across clinical, research, and public health domains; identification of adoption barriers and enablers; insights into integration with generative AI and large language models for predictive modeling, automated documentation, and decision support; and guidance for future innovations such as blockchain-enabled infrastructure and cloud-native scalability. Nonetheless, challenges remain, including uneven implementation, workforce training gaps, scalability limitations, and unresolved concerns around privacy, security, and regulatory compliance. This synthesis provides actionable insights for providers, researchers, policymakers, and developers to advance global health interoperability.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"305-336"},"PeriodicalIF":12.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0