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.
{"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}
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.
{"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}
Pub Date : 2025-12-10DOI: 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
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.
由Health Level Seven International （HL7）开发的快速医疗保健互操作性资源（FHIR）已成为领先的医疗保健数据标准，用于解决互操作性、碎片交换和不一致数据协调方面的持续障碍。随着全球卫生系统进行数字化转型，FHIR为集成电子健康记录、分析平台和决策支持工具提供了一个灵活的框架。诸如《21世纪治愈法案》（21st Century Cures Act）之类的政策命令，以及应用程序编程接口（api）、软件开发工具包（sdk）和web标准的可用性，加速了它的增长。在全球范围内，FHIR已被美国、英国、加拿大和澳大利亚的国家卫生系统采用或试点，并被纳入世界卫生组织的数据倡议，突显了其在全球数字卫生战略中的作用。本综述记录的结果包括FHIR在临床、研究和公共卫生领域的全面应用图谱；识别采用障碍和推动因素；与生成式人工智能和大型语言模型集成的见解，用于预测建模，自动化文档和决策支持；并为未来的创新提供指导，如支持区块链的基础设施和云原生可扩展性。尽管如此，挑战仍然存在，包括不均衡的实现、劳动力培训差距、可伸缩性限制以及未解决的隐私、安全性和法规遵从性问题。这种综合为提供者、研究人员、政策制定者和开发人员提供了可操作的见解，以促进全球卫生互操作性。
{"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}
Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. For each condition, we review representative methods and their quantitative results, integrating performance comparisons with analyses of data usage, model design, and task-specific adaptations, while highlighting the role of pre-trained multi-task models in achieving scalable, generalizable solutions. Finally, we propose a standardized benchmark to evaluate models across diverse datasets and improve reproducibility, emphasizing how recent innovations are transforming neurological diagnostics toward intelligent, adaptable healthcare systems.
{"title":"Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics","authors":"Jiahe Li;Xin Chen;Fanqi Shen;Junru Chen;Yuxin Liu;Daoze Zhang;Zhizhang Yuan;Fang Zhao;Meng Li;Yang Yang","doi":"10.1109/RBME.2025.3625973","DOIUrl":"10.1109/RBME.2025.3625973","url":null,"abstract":"Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. For each condition, we review representative methods and their quantitative results, integrating performance comparisons with analyses of data usage, model design, and task-specific adaptations, while highlighting the role of pre-trained multi-task models in achieving scalable, generalizable solutions. Finally, we propose a standardized benchmark to evaluate models across diverse datasets and improve reproducibility, emphasizing how recent innovations are transforming neurological diagnostics toward intelligent, adaptable healthcare systems.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"337-351"},"PeriodicalIF":12.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716225","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}
Pub Date : 2025-12-02DOI: 10.1109/RBME.2025.3636806
Yuexi Huang;Kullervo Hynynen
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.
{"title":"Technical Parameters and Feedback Control for Blood-Brain Barrier Permeability Enhancement by Focused Ultrasound","authors":"Yuexi Huang;Kullervo Hynynen","doi":"10.1109/RBME.2025.3636806","DOIUrl":"10.1109/RBME.2025.3636806","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"216-226"},"PeriodicalIF":12.0,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662286","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}
Pub Date : 2025-11-25DOI: 10.1109/RBME.2025.3632161
Lorenzo Vianello;Matthew Short;Julia Manczurowsky;Emek Barış Küçüktabak;Francesco Di Tommaso;Alessia Noccaro;Laura Bandini;Shoshana Clark;Alaina Fiorenza;Francesca Lunardini;Alberto Canton;Marta Gandolla;Alessandra L. G. Pedrocchi;Emilia Ambrosini;Manuel Murie-Fernández;Carmen B. Román;Jesus Tornero;Natacha Leon;Andrew Sawers;Jim Patton;Domenico Formica;Nevio Luigi Tagliamonte;Georg Rauter;Kilian Baur;Fabian Just;Christopher J. Hasson;Vesna D. Novak;Jose L. Pons
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.
{"title":"Robot-Mediated Physical Human–Human Interaction in Rehabilitation: A Position Paper","authors":"Lorenzo Vianello;Matthew Short;Julia Manczurowsky;Emek Barış Küçüktabak;Francesco Di Tommaso;Alessia Noccaro;Laura Bandini;Shoshana Clark;Alaina Fiorenza;Francesca Lunardini;Alberto Canton;Marta Gandolla;Alessandra L. G. Pedrocchi;Emilia Ambrosini;Manuel Murie-Fernández;Carmen B. Román;Jesus Tornero;Natacha Leon;Andrew Sawers;Jim Patton;Domenico Formica;Nevio Luigi Tagliamonte;Georg Rauter;Kilian Baur;Fabian Just;Christopher J. Hasson;Vesna D. Novak;Jose L. Pons","doi":"10.1109/RBME.2025.3632161","DOIUrl":"10.1109/RBME.2025.3632161","url":null,"abstract":"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: <italic>Robot-mediated physical Human–Human Interaction</i>. 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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"267-282"},"PeriodicalIF":12.0,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606676","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}
Pub Date : 2025-11-11DOI: 10.1109/RBME.2025.3624970
Benjamin Davidson;Franziska A Schmidt;Oliver Bichsel;Mohammad Mehdi Hajiabadi;Andres M Lozano
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.
{"title":"Transcranial Focused Ultrasound: A Transformative Tool for Intracranial Ablation, Drug Delivery, and Neuromodulation","authors":"Benjamin Davidson;Franziska A Schmidt;Oliver Bichsel;Mohammad Mehdi Hajiabadi;Andres M Lozano","doi":"10.1109/RBME.2025.3624970","DOIUrl":"10.1109/RBME.2025.3624970","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"201-215"},"PeriodicalIF":12.0,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497061","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}
Pub Date : 2025-11-06DOI: 10.1109/RBME.2025.3617858
Theekshana Dissanayake;Klaus-Robert Müller;Alexander von Lühmann
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.
{"title":"Deep Learning From Diffuse Optical Oximetry Time-Series: An fNIRS-Focused Review of Recent Advancements and Future Directions","authors":"Theekshana Dissanayake;Klaus-Robert Müller;Alexander von Lühmann","doi":"10.1109/RBME.2025.3617858","DOIUrl":"10.1109/RBME.2025.3617858","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"352-373"},"PeriodicalIF":12.0,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11230578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459669","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}
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.
{"title":"Optogenetics: Pinpoint Light on Precise Neuromodulation","authors":"Jiusi Guo;Kelvin W.K. Yeung;Chaoqiang Jiang;Liting Duan;Xianglong Han;Wei Qiao","doi":"10.1109/RBME.2025.3624697","DOIUrl":"10.1109/RBME.2025.3624697","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"86-110"},"PeriodicalIF":12.0,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459620","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}
Pub Date : 2025-10-28DOI: 10.1109/RBME.2025.3619086
Yuan Zhang;Xinfeng Zhang;Xiaoming Qi;Xinyu Wu;Feng Chen;Guanyu Yang;Huazhu Fu
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.
{"title":"Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges","authors":"Yuan Zhang;Xinfeng Zhang;Xiaoming Qi;Xinyu Wu;Feng Chen;Guanyu Yang;Huazhu Fu","doi":"10.1109/RBME.2025.3619086","DOIUrl":"10.1109/RBME.2025.3619086","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"374-395"},"PeriodicalIF":12.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393889","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}
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