Pub Date : 2022-01-07DOI: 10.1109/RBME.2022.3141369
Lekshmy Sudha Kumari;Abbas Z. Kouzani
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.
{"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}
Pub Date : 2021-12-17DOI: 10.1109/RBME.2021.3136343
Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay
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.
{"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}
Pub Date : 2021-12-14DOI: 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
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.
{"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}
Pub Date : 2021-11-30DOI: 10.1109/RBME.2021.3131358
José P. Amorim;Pedro H. Abreu;Alberto Fernández;Mauricio Reyes;João Santos;Miguel H. Abreu
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.
{"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}
Pub Date : 2021-10-26DOI: 10.1109/RBME.2021.3122522
Rui Li;Xin Yuan;Mohsen Radfar;Peter Marendy;Wei Ni;Terrence J. O’Brien;Pablo M. Casillas-Espinosa
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.
{"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}
Pub Date : 2021-10-20DOI: 10.1109/RBME.2021.3121480
Saleem Khan;Shaukat Ali;Arshad Khan;Amine Bermak
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.
{"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}
Pub Date : 2021-10-20DOI: 10.1109/RBME.2021.3121476
Daniel Ray;Tim Collins;Sandra I. Woolley;Prasad V. S. Ponnapalli
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.
{"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}
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
{"title":"Texture Analysis and Its Applications in Biomedical Imaging: A Survey","authors":"Maryam Khaksar Ghalati;Ana Nunes;Hugo Ferreira;Pedro Serranho;Rui Bernardes","doi":"10.1109/RBME.2021.3115703","DOIUrl":"10.1109/RBME.2021.3115703","url":null,"abstract":"Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"222-246"},"PeriodicalIF":17.6,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39456318","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 : 2021-09-20DOI: 10.1109/RBME.2021.3113395
Costanza Culmone;Fatih S. Yikilmaz;Fabian Trauzettel;Paul Breedveld
Conventional medical instruments are not capable of passing through tortuous anatomy as required for natural orifice transluminal endoscopic surgery due to their rigid shaft designs. Nevertheless, developments in minimally invasive surgery are pushing medical devices to become more dexterous. Amongst devices with controllable flexibility, so-called Follow-The-Leader (FTL) devices possess motion capabilities to pass through confined spaces without interacting with anatomical structures. The goal of this literature study is to provide a comprehensive overview of medical devices with FTL motion. A scientific and patent literature search was performed in five databases (Scopus, PubMed, Web of Science, IEEExplore, Espacenet). Keywords were used to isolate FTL behavior in devices with medical applications. Ultimately, 35 unique devices were reviewed and categorized. Devices were allocated according to their design strategies to obtain the three fundamental sub-functions of FTL motion: steering , (controlling the leader/end-effector orientation), propagation , (advancing the device along a specific path), and conservation (memorizing the shape of the path taken by the device). A comparative analysis of the devices was carried out, showing the commonly used design choices for each sub-function and the different combinations. The advantages and disadvantages of the design aspects and an overview of their performance were provided. Devices that were initially assessed as ineligible were considered in a possible medical context or presented with FTL potential, broadening the classification. This review could aid in the development of a new generation of FTL devices by providing a comprehensive overview of the current solutions and stimulating the search for new ones.
传统的医疗器械由于其刚性轴设计而不能像自然孔腔内内窥镜手术所需的那样穿过弯曲的解剖结构。尽管如此，微创手术的发展正在推动医疗设备变得更加灵巧。在具有可控灵活性的设备中，所谓的跟随领导者（FTL）设备具有通过受限空间而不与解剖结构相互作用的运动能力。本文献研究的目的是全面概述具有FTL运动的医疗器械。在五个数据库（Scopus、PubMed、Web of Science、IEEExplore、Espacnet）中进行了科学和专利文献检索。关键词用于隔离具有医疗应用的设备中的FTL行为。最终，对35种独特的设备进行了审查和分类。根据其设计策略对设备进行分配，以获得FTL运动的三个基本子功能：转向（控制引导器/末端执行器定向）、传播（沿特定路径推进设备）和守恒（记忆设备所走路径的形状）。对这些设备进行了比较分析，显示了每个子功能和不同组合的常用设计选择。提供了设计方面的优点和缺点以及它们的性能概述。最初被评估为不合格的器械在可能的医学背景下被考虑或具有FTL潜力，从而扩大了分类范围。这篇综述可以通过对当前解决方案进行全面概述并促进对新一代FTL设备的搜索，从而有助于开发新一代的FTL设备。
{"title":"Follow-The-Leader Mechanisms in Medical Devices: A Review on Scientific and Patent Literature","authors":"Costanza Culmone;Fatih S. Yikilmaz;Fabian Trauzettel;Paul Breedveld","doi":"10.1109/RBME.2021.3113395","DOIUrl":"10.1109/RBME.2021.3113395","url":null,"abstract":"Conventional medical instruments are not capable of passing through tortuous anatomy as required for natural orifice transluminal endoscopic surgery due to their rigid shaft designs. Nevertheless, developments in minimally invasive surgery are pushing medical devices to become more dexterous. Amongst devices with controllable flexibility, so-called Follow-The-Leader (FTL) devices possess motion capabilities to pass through confined spaces without interacting with anatomical structures. The goal of this literature study is to provide a comprehensive overview of medical devices with FTL motion. A scientific and patent literature search was performed in five databases (Scopus, PubMed, Web of Science, IEEExplore, Espacenet). Keywords were used to isolate FTL behavior in devices with medical applications. Ultimately, 35 unique devices were reviewed and categorized. Devices were allocated according to their design strategies to obtain the three fundamental sub-functions of FTL motion: \u0000<italic>steering</i>\u0000, (controlling the leader/end-effector orientation), \u0000<italic>propagation</i>\u0000, (advancing the device along a specific path), and \u0000<italic>conservation</i>\u0000 (memorizing the shape of the path taken by the device). A comparative analysis of the devices was carried out, showing the commonly used design choices for each sub-function and the different combinations. The advantages and disadvantages of the design aspects and an overview of their performance were provided. Devices that were initially assessed as ineligible were considered in a possible medical context or presented with FTL potential, broadening the classification. This review could aid in the development of a new generation of FTL devices by providing a comprehensive overview of the current solutions and stimulating the search for new ones.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"439-455"},"PeriodicalIF":17.6,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9415920","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 : 2021-09-14DOI: 10.1109/RBME.2021.3110084
Antonia Molloy;Kirsten Beaumont;Ali Alyami;Mahmut Kirimi;Daniel Hoare;Nosrat Mirzai;Hadi Heidari;Srinjoy Mitra;Steve L. Neale;John R. Mercer
Cardiovascular disease (CVD) is a group of heart and vasculature conditions which are the leading form of mortality worldwide. Blood vessels can become narrowed, restricting blood flow, and drive the majority of hearts attacks and strokes. Reactive surgical interventions are frequently required; including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Despite successful opening of vessels and restoration of blood flow, often in-stent restenosis (ISR) and graft failure can still occur, resulting in subsequent patient morbidity and mortality. A new generation of cardiovascular implants that have sensors and real-time monitoring capabilities are being developed to combat ISR and graft failure. Self-reporting stent/graft technology could enable precision medicine-based and predictive healthcare by detecting the earliest features of disease, even before symptoms occur. Bringing an implantable medical device with wireless electronic sensing capabilities to market is complex and often obstructive undertaking. This critical review analyses the obstacles that need to be overcome for self-reporting stents/grafts to be developed and provide a precision-medicine based healthcare for cardiovascular patients. Here we assess the latest research and technological advancement in the field, the current devices; including smart cardiovascular implantable biosensors and associated wireless data and power transfer solutions. We include an evaluation of devices that have reached clinical trials and the market potential for their end-user implementation.
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