IEEE Reviews in Biomedical Engineering最新文献

A Survey of Few-Shot Learning for Biomedical Time Series. 生物医学时间序列少点学习调查。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-06 DOI: 10.1109/RBME.2024.3492381

Chenqi Li, Timothy Denison, Tingting Zhu

{"title":"A Survey of Few-Shot Learning for Biomedical Time Series.","authors":"Chenqi Li, Timothy Denison, Tingting Zhu","doi":"10.1109/RBME.2024.3492381","DOIUrl":"https://doi.org/10.1109/RBME.2024.3492381","url":null,"abstract":"Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591972","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

The Physiome Project and Digital Twins. 生理组计划和数字双胞胎。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-06 DOI: 10.1109/RBME.2024.3490455

P Hunter, B de Bono, D Brooks, R Christie, J Hussan, M Lin, D Nickerson

引用次数: 0

Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey. 解决心脏数字双胞胎心电图的逆问题：调查。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-10-25 DOI: 10.1109/RBME.2024.3486439

Lei Li, Julia Camps, Blanca Rodriguez, Vicente Grau

{"title":"Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey.","authors":"Lei Li, Julia Camps, Blanca Rodriguez, Vicente Grau","doi":"10.1109/RBME.2024.3486439","DOIUrl":"https://doi.org/10.1109/RBME.2024.3486439","url":null,"abstract":"Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509871","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

Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies. 基于数据和物理驱动的深度学习快速核磁共振成像重建：基础与方法论。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-10-22 DOI: 10.1109/RBME.2024.3485022

Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Daniel Abraham, Congyu Liao, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang

{"title":"Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.","authors":"Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Daniel Abraham, Congyu Liao, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang","doi":"10.1109/RBME.2024.3485022","DOIUrl":"https://doi.org/10.1109/RBME.2024.3485022","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509870","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

Exhaled Breath Analysis: from Laboratory Test to Wearable Sensing. 呼出气体分析：从实验室测试到穿戴式传感。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-10-16 DOI: 10.1109/RBME.2024.3481360

Wenzheng Heng, Shukun Yin, Yonglin Chen, Wei Gao

{"title":"Exhaled Breath Analysis: from Laboratory Test to Wearable Sensing.","authors":"Wenzheng Heng, Shukun Yin, Yonglin Chen, Wei Gao","doi":"10.1109/RBME.2024.3481360","DOIUrl":"https://doi.org/10.1109/RBME.2024.3481360","url":null,"abstract":"Breath analysis and monitoring have emerged as pivotal components in both clinical research and daily health management, particularly in addressing the global health challenges posed by respiratory and metabolic disorders. The advancement of breath analysis strategies necessitates a multidisciplinary approach, seamlessly integrating expertise from medicine, biology, engineering, and materials science. Recent innovations in laboratory methodologies and wearable sensing technologies have ushered in an era of precise, real-time, and in situ breath analysis and monitoring. This comprehensive review elucidates the physical and chemical aspects of breath analysis, encompassing respiratory parameters and both volatile and non-volatile constituents. It emphasizes their physiological and clinical significance, while also exploring cutting-edge laboratory testing techniques and state-of-the-art wearable devices. Furthermore, the review delves into the application of sophisticated data processing technologies in the burgeoning field of breathomics and examines the potential of breath control in human-machine interaction paradigms. Additionally, it provides insights into the challenges of translating innovative laboratory and wearable concepts into mainstream clinical and daily practice. Continued innovation and interdisciplinary collaboration will drive progress in breath analysis, potentially revolutionizing personalized medicine through entirely non-invasive breath methodology.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142476965","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

Non-invasive Brain-Computer Interfaces: State of the Art and Trends. 无创脑机接口：艺术现状与趋势》。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-08-26 DOI: 10.1109/RBME.2024.3449790

Bradley J Edelman, Shuailei Zhang, Gerwin Schalk, Peter Brunner, Gernot Muller-Putz, Cuntai Guan, Bin He

引用次数: 0

Analysis and Validation of Image Search Engines in Histopathology. 组织病理学图像搜索引擎的分析与验证。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-07-12 DOI: 10.1109/RBME.2024.3425769

Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphree, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H Shah, Joaquin J Garcia, H R Tizhoosh

{"title":"Analysis and Validation of Image Search Engines in Histopathology.","authors":"Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphree, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H Shah, Joaquin J Garcia, H R Tizhoosh","doi":"10.1109/RBME.2024.3425769","DOIUrl":"https://doi.org/10.1109/RBME.2024.3425769","url":null,"abstract":"Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient tissue comparison for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient tissue comparison. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets (1269 patients) and three public datasets (1207 patients), totaling more than 200, 000 patches from 38 different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601949","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

Towards ultrasound wearable technology for cardiovascular monitoring: from device development to clinical validation. 开发用于心血管监测的超声可穿戴技术：从设备开发到临床验证。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-06-06 DOI: 10.1109/RBME.2024.3410399

AnaBelen Amado-Rey, AnaCarolina GoncalvesSeabra, Thomas Stieglitz

{"title":"Towards ultrasound wearable technology for cardiovascular monitoring: from device development to clinical validation.","authors":"AnaBelen Amado-Rey, AnaCarolina GoncalvesSeabra, Thomas Stieglitz","doi":"10.1109/RBME.2024.3410399","DOIUrl":"10.1109/RBME.2024.3410399","url":null,"abstract":"The advent of flexible, compact, energy-efficient, robust, and user-friendly wearables has significantly impacted the market growth, with an estimated value of 61.30 billion USD in 2022. Wearable sensors have revolutionized in-home health monitoring by warranting continuous measurements of vital parameters. Ultrasound is used to non-invasively, safely, and continuously record vital parameters. The next generation of smart ultrasonic devices for healthcare integrates microelectronics with flexible, stretchable patches and body-conformable devices. They offer not only wearability, and user comfort, but also higher tracking accuracy of immediate changes of cardiovascular parameters. Moreover, due to the fixed adhesion to the skin, errors derived from probe placement or patient movement are mitigated, even though placement at the correct anatomical location is still critical and requires a user's skill and knowledge. In this review, the steps required to bring wearable ultrasonic systems into the medical market (technologies, device development, signal-processing, in-lab validation, and, finally, clinical validation) are discussed. The next generation of vascular ultrasound and its future research directions offer many possibilities for modernizing vascular health assessment and the quality of personalized care for home and clinical monitoring.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141284917","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

Automated Radiology Report Generation: A Review of Recent Advances. 自动生成放射报告：最新进展回顾

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-06-03 DOI: 10.1109/RBME.2024.3408456

Phillip Sloan, Philip Clatworthy, Edwin Simpson, Majid Mirmehdi

{"title":"Automated Radiology Report Generation: A Review of Recent Advances.","authors":"Phillip Sloan, Philip Clatworthy, Edwin Simpson, Majid Mirmehdi","doi":"10.1109/RBME.2024.3408456","DOIUrl":"https://doi.org/10.1109/RBME.2024.3408456","url":null,"abstract":"Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other radiological modalities and improved evaluation methods predicted as important areas of future development.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238020","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

Alzheimer's Disease Diagnosis in the Preclinical Stage: Normal Aging or Dementia. 临床前阶段的阿尔茨海默病诊断：正常衰老还是痴呆症

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-03-13 DOI: 10.1109/RBME.2024.3376835

Fahimeh Marvi, Yun-Hsuan Chen, Mohamad Sawan

{"title":"Alzheimer's Disease Diagnosis in the Preclinical Stage: Normal Aging or Dementia.","authors":"Fahimeh Marvi, Yun-Hsuan Chen, Mohamad Sawan","doi":"10.1109/RBME.2024.3376835","DOIUrl":"https://doi.org/10.1109/RBME.2024.3376835","url":null,"abstract":"Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121021","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