IEEE Reviews in Biomedical Engineering最新文献

A Comprehensive Survey of Foundation Models in Medicine. 医学基础模型综合调查。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-05-06 DOI: 10.1109/RBME.2025.3531360

Wasif Khan, Seowung Leem, Kyle B See, Joshua K Wong, Shaoting Zhang, Ruogu Fang

{"title":"A Comprehensive Survey of Foundation Models in Medicine.","authors":"Wasif Khan, Seowung Leem, Kyle B See, Joshua K Wong, Shaoting Zhang, Ruogu Fang","doi":"10.1109/RBME.2025.3531360","DOIUrl":"10.1109/RBME.2025.3531360","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":17.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542877","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

Review of Artificial Intelligence in Lung Nodule Risk Assessment. 人工智能在肺结节风险评估中的研究进展。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-05-05 DOI: 10.1109/RBME.2025.3528946

Ying Wei, Qing Zhou, Jiaojiao Wu, Xiaoxian Xu, Yaozong Gao, Lei Chen, Yiqiang Zhan, Xiang Sean Zhou, Chengdi Wang, Feng Shi, Dinggang Shen

{"title":"Review of Artificial Intelligence in Lung Nodule Risk Assessment.","authors":"Ying Wei, Qing Zhou, Jiaojiao Wu, Xiaoxian Xu, Yaozong Gao, Lei Chen, Yiqiang Zhan, Xiang Sean Zhou, Chengdi Wang, Feng Shi, Dinggang Shen","doi":"10.1109/RBME.2025.3528946","DOIUrl":"10.1109/RBME.2025.3528946","url":null,"abstract":"Lung cancer is the leading cause of cancer-related mortality worldwide. In addition to localizing and segmenting lung nodules, a non-invasive risk assessment system can also help clinicians tailor treatment decisions in a timely manner, ultimately improving patient outcomes. Artificial intelligence (AI) technologies are increasingly being used in medical imaging to assess the risk of lung nodules, especially for malignancy classification. However, little research has been conducted on the assessment of other related risks. This work comprehensively reviews AI applications in lung nodule risk assessment, including malignancy diagnosis, pathological subtype assessment, metastasis risk evaluation, specific receptor expression identification, and disease progression tracking. It details common public databases used and state-of-the-art AI techniques, along with their benefits and challenges like data scarcity, generalizability, and interpretability. We anticipate that future research will tackle these issues, thereby increasing the improved interpretability and generalizability of AI methods in clinical workflows.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":17.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543539","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

Measures and Models of Brain-Heart Interactions. 脑-心相互作用的测量和模型。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-05-05 DOI: 10.1109/RBME.2025.3529363

Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani, Mario Chavez

{"title":"Measures and Models of Brain-Heart Interactions.","authors":"Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani, Mario Chavez","doi":"10.1109/RBME.2025.3529363","DOIUrl":"10.1109/RBME.2025.3529363","url":null,"abstract":"Exploring brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, has demonstrated enormous potential in biomarker development and neuroscientific research. A range of techniques, from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from estimating brain responses to individual heartbeats to interactions linking the heart to changes in brain organization. This review provides an overview of the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of physiological disruptions on brain-heart interactions, solidifying it as a biomarker. The vast outlook of these methods could provide tools for disease stratification in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":17.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543439","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

Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review. 血管内 OCT 图像的计算分析为未来临床提供支持：全面回顾

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-05-05 DOI: 10.1109/RBME.2025.3530244

Juhwan Lee, Yazan Gharaibeh, Pengfei Dong, Luis A P Dallan, Gabriel T R Pereira, Justin N Kim, Ammar Hoori, Linxia Gu, Hiram G Bezerra, Bernardo Cortese, David L Wilson

{"title":"Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.","authors":"Juhwan Lee, Yazan Gharaibeh, Pengfei Dong, Luis A P Dallan, Gabriel T R Pereira, Justin N Kim, Ammar Hoori, Linxia Gu, Hiram G Bezerra, Bernardo Cortese, David L Wilson","doi":"10.1109/RBME.2025.3530244","DOIUrl":"10.1109/RBME.2025.3530244","url":null,"abstract":"Over the past two decades, intravascular optical coherence tomography (IVOCT) has emerged as a promising tool for planning percutaneous coronary interventions (PCI), studying coronary artery disease, and assessing treatments. With its near-histological resolution and optical contrast, IVOCT uniquely evaluates coronary plaque characteristics, enhancing the guidance of interventional procedures. Artificial intelligence (AI) techniques have been widely applied to IVOCT imaging, providing fast and accurate automated interpretation. These techniques hold significant potential for both clinical and research purposes. Clinically, automated analysis offers comprehensive assessments of coronary plaques, leading to better treatment decisions during PCI. For research, automated interpretation of IVOCT opens new avenues to understand the pathophysiology of coronary atherosclerosis. However, these techniques face several limitations, including issues related to spatial resolution, challenges in manual assessments, and the additional time required for these analyses. This review covers recent advancements and applications of AI techniques and computational simulation methods in IVOCT image analysis, including vessel wall segmentation, plaque characterization, stent analysis, and their clinical applications. Furthermore, we discuss the potential of AI-enhanced IVOCT analysis to facilitate personalized decision-making, potentially improving short- and long-term patient outcomes.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":17.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543139","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

Principles and Operation of Virtual Brain Twins. 虚拟脑双胞胎原理与操作。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-04-21 DOI: 10.1109/RBME.2025.3562951

Meysam Hashemi, Damien Depannemaecker, Marisa Saggio, Paul Triebkorn, Giovanni Rabuffo, Jan Fousek, Abolfazl Ziaeemehr, Viktor Sip, Anastasios Athanasiadis, Martin Breyton, Marmaduke Woodman, Huifang Wang, Spase Petkoski, Pierpaolo Sorrentino, Viktor Jirsa

{"title":"Principles and Operation of Virtual Brain Twins.","authors":"Meysam Hashemi, Damien Depannemaecker, Marisa Saggio, Paul Triebkorn, Giovanni Rabuffo, Jan Fousek, Abolfazl Ziaeemehr, Viktor Sip, Anastasios Athanasiadis, Martin Breyton, Marmaduke Woodman, Huifang Wang, Spase Petkoski, Pierpaolo Sorrentino, Viktor Jirsa","doi":"10.1109/RBME.2025.3562951","DOIUrl":"https://doi.org/10.1109/RBME.2025.3562951","url":null,"abstract":"Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":17.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062650","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

IEEE Reviews in Biomedical Engineering (R-BME) IEEE生物医学工程评论（R-BME）

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518719

引用次数: 0

Editorial: Harnessing Reviews to Advance Biomedical Engineering's New Horizons 社论：利用评论推进生物医学工程的新视野

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518852

Bin He

引用次数: 0

IEEE Engineering in Medicine and Biology Society 医学与生物工程学会

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518715

引用次数: 0

Advancing Cardiac Organoid Engineering Through Application of Biophysical Forces 应用生物物理力推进心脏类器官工程

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-12-09 DOI: 10.1109/RBME.2024.3514378

Adriana Blazeski;Guillermo García-Cardeña;Roger D. Kamm

{"title":"Advancing Cardiac Organoid Engineering Through Application of Biophysical Forces","authors":"Adriana Blazeski;Guillermo García-Cardeña;Roger D. Kamm","doi":"10.1109/RBME.2024.3514378","DOIUrl":"https://doi.org/10.1109/RBME.2024.3514378","url":null,"abstract":"Cardiac organoids represent an important bioengineering opportunity in the development of models to study human heart pathophysiology. By incorporating multiple cardiac cell types in three-dimensional culture and developmentally-guided biochemical signaling, cardiac organoids recapitulate numerous features of heart tissue. However, cardiac tissue also experiences a variety of mechanical forces as the heart develops and over the course of each contraction cycle. It is now clear that these forces impact cellular specification, phenotype, and function, and should be incorporated into the engineering of cardiac organoids in order to generate better models. In this review, we discuss strategies for engineering cardiac organoids and report the effects of organoid design on the function of cardiac cells. We then discuss the mechanical environment of the heart, including forces arising from tissue elasticity, contraction, blood flow, and stretch, and report on efforts to mimic these biophysical cues in cardiac organoids. Finally, we review emerging areas of cardiac organoid research, for the study of cardiac development, the formation of multi-organ models, and the simulation of the effects of spaceflight on cardiac tissue, and consider how these investigations might benefit from the inclusion of mechanical cues.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"211-230"},"PeriodicalIF":17.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105751","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

Earable Multimodal Sensing and Stimulation: A Prospective Toward Unobtrusive Closed-Loop Biofeedback 可听的多模态传感和刺激：对不显眼的闭环生物反馈的展望

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-29 DOI: 10.1109/RBME.2024.3508713

Yuchen Xu;Abhinav Uppal;Min Suk Lee;Kuldeep Mahato;Brian L. Wuerstle;Muyang Lin;Omeed Djassemi;Tao Chen;Rui Lin;Akshay Paul;Soumil Jain;Florian Chapotot;Esra Tasali;Patrick Mercier;Sheng Xu;Joseph Wang;Gert Cauwenberghs

{"title":"Earable Multimodal Sensing and Stimulation: A Prospective Toward Unobtrusive Closed-Loop Biofeedback","authors":"Yuchen Xu;Abhinav Uppal;Min Suk Lee;Kuldeep Mahato;Brian L. Wuerstle;Muyang Lin;Omeed Djassemi;Tao Chen;Rui Lin;Akshay Paul;Soumil Jain;Florian Chapotot;Esra Tasali;Patrick Mercier;Sheng Xu;Joseph Wang;Gert Cauwenberghs","doi":"10.1109/RBME.2024.3508713","DOIUrl":"https://doi.org/10.1109/RBME.2024.3508713","url":null,"abstract":"The human ear has emerged as a bidirectional gateway to the brain's and body's signals. Recent advances in around-the-ear and in-ear sensors have enabled the assessment of biomarkers and physiomarkers derived from brain and cardiac activity using ear-electroencephalography (ear-EEG), photoplethysmography (ear-PPG), and chemical sensing of analytes from the ear, with ear-EEG having been taken beyond-the-lab to outer space. Parallel advances in non-invasive and minimally invasive brain stimulation techniques have leveraged the ear's access to two cranial nerves to modulate brain and body activity. The vestibulocochlear nerve stimulates the auditory cortex and limbic system with sound, while the auricular branch of the vagus nerve indirectly but significantly couples to the autonomic nervous system and cardiac output. Acoustic and current mode stimuli delivered using discreet and unobtrusive earables are an active area of research, aiming to make biofeedback and bioelectronic medicine deliverable outside of the clinic, with remote and continuous monitoring of therapeutic responsivity and long-term adaptation. Leveraging recent advances in ear-EEG, transcutaneous auricular vagus nerve stimulation (taVNS), and unobtrusive acoustic stimulation, we review accumulating evidence that combines their potential into an integrated earable platform for closed-loop multimodal sensing and neuromodulation, towards personalized and holistic therapies that are near, in- and around-the-ear.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"5-25"},"PeriodicalIF":17.2,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105660","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