IEEE Reviews in Biomedical Engineering最新文献_第9页

Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions 血液动力学建模、医学成像和机器学习及其在心血管干预中的应用

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2022-01-11 DOI: 10.1109/RBME.2022.3142058

Mason Kadem;Louis Garber;Mohamed Abdelkhalek;Baraa K. Al-Khazraji;Zahra Keshavarz-Motamed

{"title":"Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions","authors":"Mason Kadem;Louis Garber;Mohamed Abdelkhalek;Baraa K. Al-Khazraji;Zahra Keshavarz-Motamed","doi":"10.1109/RBME.2022.3142058","DOIUrl":"10.1109/RBME.2022.3142058","url":null,"abstract":"Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"403-423"},"PeriodicalIF":17.6,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9364000","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: 13

Advances in Non-Invasive Blood Pressure Measurement Techniques 无创血压测量技术研究进展

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2022-01-11 DOI: 10.1109/RBME.2022.3141877

Tuukka Panula;Jukka-Pekka Sirkiä;David Wong;Matti Kaisti

{"title":"Advances in Non-Invasive Blood Pressure Measurement Techniques","authors":"Tuukka Panula;Jukka-Pekka Sirkiä;David Wong;Matti Kaisti","doi":"10.1109/RBME.2022.3141877","DOIUrl":"10.1109/RBME.2022.3141877","url":null,"abstract":"Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"424-438"},"PeriodicalIF":17.6,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09677909.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363999","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: 13

Electrophysiology-Based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects 基于电生理的闭环光遗传学脑刺激装置的研究进展与展望

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2022-01-07 DOI: 10.1109/RBME.2022.3141369

Lekshmy Sudha Kumari;Abbas Z. Kouzani

{"title":"Electrophysiology-Based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects","authors":"Lekshmy Sudha Kumari;Abbas Z. Kouzani","doi":"10.1109/RBME.2022.3141369","DOIUrl":"10.1109/RBME.2022.3141369","url":null,"abstract":"With its potential of single cell specificity, optogenetics has made the investigation into the brain circuits more controllable. Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic brain stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"91-108"},"PeriodicalIF":17.6,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363998","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: 3

A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation 形状约束深度学习在医学图像分割中的应用综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-12-17 DOI: 10.1109/RBME.2021.3136343

Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay

{"title":"A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation","authors":"Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay","doi":"10.1109/RBME.2021.3136343","DOIUrl":"10.1109/RBME.2021.3136343","url":null,"abstract":"Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"225-240"},"PeriodicalIF":17.6,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9416394","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: 12

Exploring the Potential of Stem Cell-Based Therapy for Aesthetic and Plastic Surgery 探索以干细胞为基础的美容整形治疗潜力

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-12-14 DOI: 10.1109/RBME.2021.3134994

Dang-Khoa Tran;Thuy Nguyen Thi Phuong;Nhat-Le Bui;Vijai Singh;Qi Hao Looi;Benson Koh;Ungku Mohd Shahrin B Mohd Zaman;Jhi Biau Foo;Chia-Ching Wu;Pau Loke Show;Dinh-Toi Chu

{"title":"Exploring the Potential of Stem Cell-Based Therapy for Aesthetic and Plastic Surgery","authors":"Dang-Khoa Tran;Thuy Nguyen Thi Phuong;Nhat-Le Bui;Vijai Singh;Qi Hao Looi;Benson Koh;Ungku Mohd Shahrin B Mohd Zaman;Jhi Biau Foo;Chia-Ching Wu;Pau Loke Show;Dinh-Toi Chu","doi":"10.1109/RBME.2021.3134994","DOIUrl":"10.1109/RBME.2021.3134994","url":null,"abstract":"Over the last decade, stem cell-associated therapies are widely used because of their potential in self-renewable and multipotent differentiation ability. Stem cells have become more attractive for aesthetic uses and plastic surgery, including scar reduction, breast augmentation, facial contouring, hand rejuvenation, and anti-aging. The current preclinical and clinical studies of stem cells on aesthetic uses also showed promising outcomes. Adipose-derived stem cells are commonly used for fat grafting that demonstrated scar improvement, anti-aging, skin rejuvenation properties, etc. While stem cell-based products have yet to receive approval from the FDA for aesthetic medicine and plastic surgery. Moving forward, the review on the efficacy and potential of stem cell-based therapy for aesthetic and plastic surgery is limited. In the present review, we discuss the current status and recent advances of using stem cells for aesthetic and plastic surgery. The potential of cell-free therapy and tissue engineering in this field is also highlighted. The clinical applications, advantages, and limitations are also discussed. This review also provides further works that need to be investigated to widely apply stem cells in the clinic, especially in aesthetic and plastic contexts.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"386-402"},"PeriodicalIF":17.6,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9713742","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: 6

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists 解读深度机器学习模型：肿瘤学家的简单指南

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-11-30 DOI: 10.1109/RBME.2021.3131358

José P. Amorim;Pedro H. Abreu;Alberto Fernández;Mauricio Reyes;João Santos;Miguel H. Abreu

{"title":"Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists","authors":"José P. Amorim;Pedro H. Abreu;Alberto Fernández;Mauricio Reyes;João Santos;Miguel H. Abreu","doi":"10.1109/RBME.2021.3131358","DOIUrl":"10.1109/RBME.2021.3131358","url":null,"abstract":"Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using “deep learning techniques,” “interpretability” and “oncology” as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"192-207"},"PeriodicalIF":17.6,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9358596","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: 8

Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review 生物数据的图形信号处理、图形神经网络和图形学习：系统综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-10-26 DOI: 10.1109/RBME.2021.3122522

Rui Li;Xin Yuan;Mohsen Radfar;Peter Marendy;Wei Ni;Terrence J. O’Brien;Pablo M. Casillas-Espinosa

{"title":"Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review","authors":"Rui Li;Xin Yuan;Mohsen Radfar;Peter Marendy;Wei Ni;Terrence J. O’Brien;Pablo M. Casillas-Espinosa","doi":"10.1109/RBME.2021.3122522","DOIUrl":"10.1109/RBME.2021.3122522","url":null,"abstract":"Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews graph-based analysis methods of Graph Signal Processing (GSP), Graph Neural Networks (GNNs) and graph topology inference, and their applications to biological data. This work focuses on the algorithms of graph-based approaches and the constructions of graph-based frameworks that are adapted to a broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological signals in the graph domain that can potentially benefit from the underlying graph structures. We also review the node, graph, and interaction oriented applications of GNNs with inductive and transductive learning manners for various biological targets. As a key component of graph analysis, we provide a review of graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within this exhaustive literature collection, potentially providing insights for future research in biological sciences.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"109-135"},"PeriodicalIF":17.6,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9358765","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: 19

Wearable Printed Temperature Sensors: Short Review on Latest Advances for Biomedical Applications 可穿戴印刷温度传感器：生物医学应用最新进展综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-10-20 DOI: 10.1109/RBME.2021.3121480

Saleem Khan;Shaukat Ali;Arshad Khan;Amine Bermak

{"title":"Wearable Printed Temperature Sensors: Short Review on Latest Advances for Biomedical Applications","authors":"Saleem Khan;Shaukat Ali;Arshad Khan;Amine Bermak","doi":"10.1109/RBME.2021.3121480","DOIUrl":"10.1109/RBME.2021.3121480","url":null,"abstract":"The rapid growth in wearable biosensing devices is driven by the strong desire to monitor the human health data and to predict the symptoms of chronic diseases at an early stage. Different sensors are developed for continuous monitoring of various biomarkers through wearable and implantable sensing patches. Temperature sensor has proved to be an important physiological parameter amongst the various wearable biosensing patches. This paper highlights the recent progresses made in printing of functional nanomaterials for developing wearable temperature sensors on polymeric substrates. A special focus is given to the advanced functional nanomaterials as well as their deposition through printing technologies. The geometric resolutions, shape, physical and electrical characteristics as well as sensing properties using different materials are compared and summarized. Wearability is the main concern of these newly developed sensors, which is summarized by discussing representative examples. Finally, the challenges concerning the stability, repeatability, reliability, sensitivity, linearity, ageing, and large-scale manufacturing are discussed with future outlook of the wearable systems.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"152-170"},"PeriodicalIF":17.6,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09582797.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363996","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: 7

A Review of Wearable Multi-Wavelength Photoplethysmography 可佩戴多波长光体积描记术综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-10-20 DOI: 10.1109/RBME.2021.3121476

Daniel Ray;Tim Collins;Sandra I. Woolley;Prasad V. S. Ponnapalli

{"title":"A Review of Wearable Multi-Wavelength Photoplethysmography","authors":"Daniel Ray;Tim Collins;Sandra I. Woolley;Prasad V. S. Ponnapalli","doi":"10.1109/RBME.2021.3121476","DOIUrl":"10.1109/RBME.2021.3121476","url":null,"abstract":"Optical pulse detection ‘photoplethysmography’ (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"136-151"},"PeriodicalIF":17.6,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363994","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: 29

Texture Analysis and Its Applications in Biomedical Imaging: A Survey 纹理分析及其在生物医学成像中的应用综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2021-09-27 DOI: 10.1109/RBME.2021.3115703

Maryam Khaksar Ghalati;Ana Nunes;Hugo Ferreira;Pedro Serranho;Rui Bernardes

引用次数: 11