Pub Date : 2024-02-22DOI: 10.1109/RBME.2024.3368662
Robert G. Gloeb-McDonald;Gene Y. Fridman
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 10 µW to 100 mW 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.
{"title":"Glucose Fuel Cells: Electricity From Blood Sugar","authors":"Robert G. Gloeb-McDonald;Gene Y. Fridman","doi":"10.1109/RBME.2024.3368662","DOIUrl":"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 10 µW to 100 mW 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":"18 ","pages":"268-280"},"PeriodicalIF":17.2,"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}
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.
自 2020 年以来，乳腺癌的发病率已成为全球所有恶性肿瘤中最高的。乳腺成像在早期诊断和干预以改善乳腺癌患者的预后方面发挥着重要作用。近十年来，深度学习在乳腺癌成像分析领域取得了显著进展，在解读乳腺成像模式的丰富信息和复杂背景方面大有可为。考虑到深度学习技术的飞速进步和乳腺癌的日益严重，总结过去的进展并确定未来需要应对的挑战至关重要。本文对基于深度学习的乳腺癌成像研究进行了广泛回顾，涵盖了过去十年间对乳房 X 线照片、超声波、磁共振成像和数字病理图像的研究。本文阐述并讨论了基于成像的筛查、诊断、治疗反应预测和预后方面的主要深度学习方法和应用。根据调查结果，我们对基于深度学习的乳腺癌成像未来研究面临的挑战和潜在途径进行了全面讨论。
{"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":"18 ","pages":"130-151"},"PeriodicalIF":17.2,"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}
Pub Date : 2024-01-19DOI: 10.1109/RBME.2024.3355875
Neil Upreti;Geonsoo Jin;Joseph Rich;Ruoyu Zhong;John Mai;Chenglong Zhao;Tony Jun Huang
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.
{"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":"18 ","pages":"337-349"},"PeriodicalIF":17.2,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409540","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.1109/RBME.2023.3333510
{"title":"IEEE Engineering in Medicine and Biology Society Information","authors":"","doi":"10.1109/RBME.2023.3333510","DOIUrl":"https://doi.org/10.1109/RBME.2023.3333510","url":null,"abstract":"","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"C2-C2"},"PeriodicalIF":17.6,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10398579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434807","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 : 2024-01-09DOI: 10.1109/RBME.2024.3351713
Jiaming Cui;Neal A. Hollingsworth;Steven M. Wright
The shortened radio frequency wavelength in high field MRI makes it challenging to create a uniform excitation pattern over a large field of view, or to achieve satisfactory transmission efficiency at a local area. Transmit arrays are one tool that can be used to create a desired excitation pattern. To be effective, it is important to be able to control the current amplitude and phase at the array elements. The control of the current may get complicated by the coil coupling in many applications. Various methods have been proposed to achieve current control, either in the presence of coupling, or by effectively decouple the array elements. These methods are applied in different subsystems in the RF transmission chain: coil; coil-amplifier interface; amplifier, etc. In this review paper, we provide an overview of the various approaches and aspects of transmit current control and decoupling.
{"title":"A Review of Current Control and Decoupling Methods for MRI Transmit Arrays","authors":"Jiaming Cui;Neal A. Hollingsworth;Steven M. Wright","doi":"10.1109/RBME.2024.3351713","DOIUrl":"10.1109/RBME.2024.3351713","url":null,"abstract":"The shortened radio frequency wavelength in high field MRI makes it challenging to create a uniform excitation pattern over a large field of view, or to achieve satisfactory transmission efficiency at a local area. Transmit arrays are one tool that can be used to create a desired excitation pattern. To be effective, it is important to be able to control the current amplitude and phase at the array elements. The control of the current may get complicated by the coil coupling in many applications. Various methods have been proposed to achieve current control, either in the presence of coupling, or by effectively decouple the array elements. These methods are applied in different subsystems in the RF transmission chain: coil; coil-amplifier interface; amplifier, etc. In this review paper, we provide an overview of the various approaches and aspects of transmit current control and decoupling.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"388-400"},"PeriodicalIF":17.2,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10384716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139404646","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 : 2023-11-13DOI: 10.1109/RBME.2023.3332164
Bin He
2023 has been a year of growth and transformation for IEEE Reviews in Biomedical Engineering (RBME). Thanks to our authors, reviewers, and editorial board members, RBME received strong metrics on Impact Factor and CiteScore reaching 17.6 and 27.8 respectively, which places RBME in the top 3 according to the Impact Factor, and the top 4 according to the CiteScore in all Biomedical Engineering Journals/Publications. We have also observed substantially increasing submissions in the past year. To better serve our authors, we have implemented a screening process to quickly communicate the outcome of assessment, and allow the authors to submit manuscripts which do not fit the scope or have a low chance of passing through the highly selective review process, to find a more suitable journal in a timely manner.
{"title":"Editorial: On the Writing of a Scientific Review Article","authors":"Bin He","doi":"10.1109/RBME.2023.3332164","DOIUrl":"10.1109/RBME.2023.3332164","url":null,"abstract":"2023 has been a year of growth and transformation for IEEE Reviews in Biomedical Engineering (RBME). Thanks to our authors, reviewers, and editorial board members, RBME received strong metrics on Impact Factor and CiteScore reaching 17.6 and 27.8 respectively, which places RBME in the top 3 according to the Impact Factor, and the top 4 according to the CiteScore in all Biomedical Engineering Journals/Publications. We have also observed substantially increasing submissions in the past year. To better serve our authors, we have implemented a screening process to quickly communicate the outcome of assessment, and allow the authors to submit manuscripts which do not fit the scope or have a low chance of passing through the highly selective review process, to find a more suitable journal in a timely manner.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"3-3"},"PeriodicalIF":17.6,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10315188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92156913","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 : 2023-11-09DOI: 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
Objective: 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. Methods: 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. Significance: 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.
{"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:</i>\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:</i>\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:</i>\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":"17 ","pages":"19-41"},"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}
Pub Date : 2023-10-12DOI: 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
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.
{"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":"17 ","pages":"80-97"},"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}
Pub Date : 2023-08-28DOI: 10.1109/RBME.2023.3309328
F. Barberi;E. Anselmino;A. Mazzoni;M. Goldfarb;S. Micera
The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users’ needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.
{"title":"Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses","authors":"F. Barberi;E. Anselmino;A. Mazzoni;M. Goldfarb;S. Micera","doi":"10.1109/RBME.2023.3309328","DOIUrl":"10.1109/RBME.2023.3309328","url":null,"abstract":"The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users’ needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"212-228"},"PeriodicalIF":17.6,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10232905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10483877","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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