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.
{"title":"Toward Clinical Applications of Intelligent Robotic Ultrasound Systems","authors":"Taiyu Han;Guochen Ning;Hanying Liang;Zihan Li;Zhongliang Jiang;Fang Chen;Yan Kang;Jianwen Luo;Hongen Liao","doi":"10.1109/RBME.2025.3610605","DOIUrl":"10.1109/RBME.2025.3610605","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"227-247"},"PeriodicalIF":12.0,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214259","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 : 2025-09-12DOI: 10.1109/RBME.2025.3593185
Arnault H Caillet;Andrew T M Phillips;Christopher Carty;Dario Farina;Luca Modenese
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.
{"title":"Hill-Type Models of Skeletal Muscle and Neuromuscular Actuators: A Systematic Review","authors":"Arnault H Caillet;Andrew T M Phillips;Christopher Carty;Dario Farina;Luca Modenese","doi":"10.1109/RBME.2025.3593185","DOIUrl":"10.1109/RBME.2025.3593185","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"159-181"},"PeriodicalIF":12.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11162721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145055984","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 : 2025-07-03DOI: 10.1109/RBME.2025.3577587
Luoting Zhuang;Stephen H. Park;Steven J. Skates;Ashley E. Prosper;Denise R. Aberle;William Hsu
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.
{"title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","authors":"Luoting Zhuang;Stephen H. Park;Steven J. Skates;Ashley E. Prosper;Denise R. Aberle;William Hsu","doi":"10.1109/RBME.2025.3577587","DOIUrl":"10.1109/RBME.2025.3577587","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"182-200"},"PeriodicalIF":12.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561446","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}
Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based 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 brain-machine integration.
{"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":"10.1109/RBME.2025.3562951","url":null,"abstract":"Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based 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 brain-machine integration.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"19 ","pages":"111-139"},"PeriodicalIF":12.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972118","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1109/RBME.2024.3518715
{"title":"IEEE Engineering in Medicine and Biology Society","authors":"","doi":"10.1109/RBME.2024.3518715","DOIUrl":"https://doi.org/10.1109/RBME.2024.3518715","url":null,"abstract":"","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"C2-C2"},"PeriodicalIF":17.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105657","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 : 2025-01-23DOI: 10.1109/RBME.2025.3529363
Diego Candia-Rivera;Luca Faes;Fabrizio de Vico Fallani;Mario Chavez
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.
{"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":"19 ","pages":"24-40"},"PeriodicalIF":12.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851428","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1109/RBME.2025.3531360
Wasif Khan;Seowung Leem;Kyle B. See;Joshua K. Wong;Shaoting Zhang;Ruogu Fang
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.
{"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":"19 ","pages":"283-304"},"PeriodicalIF":12.0,"publicationDate":"2025-01-20","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}
Pub Date : 2025-01-16DOI: 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
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.
{"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":"19 ","pages":"396-411"},"PeriodicalIF":12.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341395/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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