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

Glucose Fuel Cells: Electricity from Blood Sugar. 葡萄糖燃料电池：利用血糖发电

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-02-22 DOI: 10.1109/RBME.2024.3368662

Robert G Gloeb-McDonald, Gene Fridman

{"title":"Glucose Fuel Cells: Electricity from Blood Sugar.","authors":"Robert G Gloeb-McDonald, Gene Fridman","doi":"10.1109/RBME.2024.3368662","DOIUrl":"https://doi.org/10.1109/RBME.2024.3368662","url":null,"abstract":"Harvesting energy from the human body is an area of growing interest. While several techniques have been explored, the focus in the field is converging on using Glucose Fuel Cells (GFCs) that use glucose oxidation reactions at an anode and oxygen reduction reactions (ORRs) at a cathode to create a voltage gradient that can be stored as power. To facilitate these reactions, catalysts are immobilized at an anode and cathode that result in electrochemistry that typically produces two electrons, a water molecule, and gluconic acid. There are two competing classes of these catalysts: enzymes, which use organic proteins, and abiotic options, which use reactive metals. Enzymatic catalysts show better specificity towards glucose, whereas abiotic options show superior operational stability. The most advanced enzymatic test showed a maximum power density of 119 μW/cm2 and an efficiency loss of 4% over 15 hours of operation. The best abiotic experiment resulted in 43 μW/cm2 and exhibited no signs of performance loss after 140 hours. Given the range of existing implantable devices' power budget from 10μW to 100mW and expected operational duration of 10 years or more, GFCs hold promise, but considerable advances need to be made to translate this technology to practical applications.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139933396","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

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions. 乳腺癌成像中的深度学习：十年进展与未来方向》。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-01-24 DOI: 10.1109/RBME.2024.3357877

Luyang Luo, Xi Wang, Yi Lin, Xiaoqi Ma, Andong Tan, Ronald Chan, Varut Vardhanabhuti, Winnie Cw Chu, Kwang-Ting Cheng, Hao Chen

{"title":"Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.","authors":"Luyang Luo, Xi Wang, Yi Lin, Xiaoqi Ma, Andong Tan, Ronald Chan, Varut Vardhanabhuti, Winnie Cw Chu, Kwang-Ting Cheng, Hao Chen","doi":"10.1109/RBME.2024.3357877","DOIUrl":"10.1109/RBME.2024.3357877","url":null,"abstract":"Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139546507","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

Advances in Microsphere-based Super-resolution Imaging. 基于微球的超分辨率成像技术的进展。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-01-19 DOI: 10.1109/RBME.2024.3355875

Neil Upreti, Geonsoo Jin, Joseph Rich, Ruoyu Zhong, John Mai, Chenglong Zhao, Tony Jun Huang

{"title":"Advances in Microsphere-based Super-resolution Imaging.","authors":"Neil Upreti, Geonsoo Jin, Joseph Rich, Ruoyu Zhong, John Mai, Chenglong Zhao, Tony Jun Huang","doi":"10.1109/RBME.2024.3355875","DOIUrl":"10.1109/RBME.2024.3355875","url":null,"abstract":"Techniques to resolve images beyond the diffraction limit of light with a large field of view (FOV) are necessary to foster progress in various fields such as cell and molecular biology, biophysics, and nanotechnology, where nanoscale resolution is crucial for understanding the intricate details of large-scale molecular interactions. Although several means of achieving super-resolutions exist, they are often hindered by factors such as high costs, significant complexity, lengthy processing times, and the classical tradeoff between image resolution and FOV. Microsphere-based super-resolution imaging has emerged as a promising approach to address these limitations. In this review, we delve into the theoretical underpinnings of microsphere-based imaging and the associated photonic nanojet. This is followed by a comprehensive exploration of various microsphere-based imaging techniques, encompassing static imaging, mechanical scanning, optical scanning, and acoustofluidic scanning methodologies. This review concludes with a forward-looking perspective on the potential applications and future scientific directions of this innovative technology.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503074","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 Engineering in Medicine and Biology Society Information IEEE 医学与生物学工程学会信息

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-01-12 DOI: 10.1109/RBME.2023.3333510

引用次数: 0

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

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-01-12 DOI: 10.1109/RBME.2023.3333516

引用次数: 0

A Review of Current Control and Decoupling Methods for MRI Transmit Arrays. 核磁共振成像发射阵列的电流控制和去耦方法综述。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-01-09 DOI: 10.1109/RBME.2024.3351713

Jiaming Cui, Neal A Hollingsworth, Steven M Wright

引用次数: 0

Editorial: On the Writing of a Scientific Review Article 社论:关于科学评论文章的写作。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-11-13 DOI: 10.1109/RBME.2023.3332164

Bin He

引用次数: 0

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities 改善糖尿病血糖控制的人工智能和机器学习：最佳实践、陷阱和机遇。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-11-09 DOI: 10.1109/RBME.2023.3331297

Peter G. Jacobs;Pau Herrero;Andrea Facchinetti;Josep Vehi;Boris Kovatchev;Marc D. Breton;Ali Cinar;Konstantina S. Nikita;Francis J. Doyle;Jorge Bondia;Tadej Battelino;Jessica R. Castle;Konstantia Zarkogianni;Rahul Narayan;Clara Mosquera-Lopez

{"title":"Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities","authors":"Peter G. Jacobs;Pau Herrero;Andrea Facchinetti;Josep Vehi;Boris Kovatchev;Marc D. Breton;Ali Cinar;Konstantina S. Nikita;Francis J. Doyle;Jorge Bondia;Tadej Battelino;Jessica R. Castle;Konstantia Zarkogianni;Rahul Narayan;Clara Mosquera-Lopez","doi":"10.1109/RBME.2023.3331297","DOIUrl":"10.1109/RBME.2023.3331297","url":null,"abstract":"<italic>Objective:\u0000 Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. \u0000<italic>Methods:\u0000 Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. \u0000<italic>Significance:\u0000 These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence 利用先进的人工智能将多组学数据与EHR集成用于精准医学。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-10-12 DOI: 10.1109/RBME.2023.3324264

Li Tong;Wenqi Shi;Monica Isgut;Yishan Zhong;Peter Lais;Logan Gloster;Jimin Sun;Aniketh Swain;Felipe Giuste;May D. Wang

{"title":"Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence","authors":"Li Tong;Wenqi Shi;Monica Isgut;Yishan Zhong;Peter Lais;Logan Gloster;Jimin Sun;Aniketh Swain;Felipe Giuste;May D. Wang","doi":"10.1109/RBME.2023.3324264","DOIUrl":"10.1109/RBME.2023.3324264","url":null,"abstract":"With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses 开发以用户为中心的神经集成下肢假肢。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-08-28 DOI: 10.1109/RBME.2023.3309328

F. Barberi;E. Anselmino;A. Mazzoni;M. Goldfarb;S. Micera

引用次数: 0