检索结果-内蒙古大学图书馆

Indicators to assess physiological heat strain–Part 3: Multi-country field evaluation and consensus recommendations

Temperature 2022年第3期9卷 274-291页

作者： Ioannou, Leonidas G. Tsoutsoubi, Lydia Mantzios, Konstantinos Vliora, Maria Nintou, Eleni Piil, Jacob F. Notley, Sean R. Dinas, Petros C. Gourzoulidis, George A. Havenith, George Brearley, Matt Mekjavic, Igor B. Kenny, Glen P. Nybo, Lars Flouris, Andreas D. FAME Laboratory Department of Physical Education and Sport Science University of Thessaly Trikala Greece Department of Nutrition Exercise and Sports August Krogh Building University of Copenhagen Denmark Hellenic Ministry of Labour and Social Affairs Athens Greece Environmental Ergonomics Research Centre Loughborough Design School Loughborough University Loughborough United Kingdom National Critical Care and Trauma Response Centre Royal Darwin Hospital Darwin NT Australia Thermal Hyperformance Pty Ltd Takura QLD Australia Department of Automation and Robotics Jozef Stefan Institute Slovenia Human and Environmental Physiology Research Unit Faculty of Health Sciences University of Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa ON Canada

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this third paper, we conducted field experiments across nine countries to evaluate the efficacy of 61 meteorology-based TSIs for assessing the physiological strain experienced by individuals working in the heat. We monitored 372 experi-enced and acclimatized workers during 893 full work shifts. We continuously assessed core body temperature, mean skin temperature, and heart rate data together with pre/post urine specific gravity and color. The TSIs were evaluated against 17 published criteria covering physiological parameters, practicality, cost effectiveness, and health guidance issues. Simple meteorological parameters explained only a fraction of the variance in physiological heat strain (R² = 0.016 to 0.427;p < 0.001), reflecting the importance of adopting more sophisticated TSIs. Nearly all TSIs correlated with mean skin temperature (98%), mean body temperature (97%), and heart rate (92%), while 66% of TSIs correlated with the magnitude of dehydration and 59% correlated with core body temperature (r = 0.031 to 0.602;p < 0.05). When evaluated against the 17 published criteria, the TSIs scored from 4.7 to 55.4% (max score = 100%). The indoor (55.4%) and outdoor (55.1%) Wet-Bulb Globe Temperature and the Universal Thermal Climate Index (51.7%) scored higher compared to other TSIs (4.7 to 42.0%). Therefore, these three TSIs have the highest potential to assess the physiological strain experienced by individuals working in the heat. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： core temperature criteria dehydration heart rate heat indices heat strain hyperthermia labor Occupational skin temperature temperature thermal indices work

来源：评论

学校读者我要写书评

暂无评论

Clamping Holder of Replaceable Tools for Medical Robot*

Clamping Holder of Replaceable Tools for Medical Robot*

引用

WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yinjia Zhang Qian Yang Dengbo Zhang Xudong Ma Jie Tang Yuanzheng Hou Yida David Hu Junchen Wang Changsheng Li Shaolong Kuang Wenyong Liu Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China Key Laboratory for Biomechaniscs and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

The replacement of surgical tools is one of the basic operations in performing surgery. However, this step is time-consuming in operational processes, and elevates the associated risks. Reason: The revision enhances clarity and employs more formal language suitable for an academic context. At present, medical robots still need manual operation to replace surgical tools. In order to enable the robot to change surgical tools, this study designed a clamping tool holder for changing surgical tools. The clamping tool holder is driven by a 6-degree of freedom robotic arm. In this paper, CAD model of the clamping tool holder is presented, the motion of the clamping tool holder analysis and pick up the tools of simulation operation. Finally, the prototype experiment verified that the clamping tool holder can hold and release surgical tools without manual assistance. The structure can realize the effect of saving manpower and improving operation efficiency.

关键词： Solid modeling Analytical models Medical robotics Prototypes Surgery Formal languages Manuals

来源：评论

学校读者我要写书评

暂无评论

Single Segment Controllable Continuum Robot: A Pilot Study*

Single Segment Controllable Continuum Robot: A Pilot Study*

引用

WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Xu-Dong Ma Qian Yang Fengtong Wu Yinjia Zhang Yida David Hu Wenyong Liu Junchen Wang Changsheng Li Jie Tang Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

In existing continuum robots, not every segment within a section is controlled, limiting the full utilization of their flexibility and making navigation through narrow and continuously curved spaces challenging. This paper introduces a single-section controllable continuum robot where each segment can be independently controlled via driving wires. Firstly, we designe the structure and connection method of the continuum robot segments. Secondly, we establish the kinematic model of the single-section controllable continuum robot, enabling independent control of each segment through the driving wires. Finally, we compares and tests the fitting accuracy of different configurations of continuum robots with controllable parts containing 1, 3, 5, and 7 segments in one section, regarding their ability to fit the same curve. The single-section controllable continuum robot can independently control the position of each joint within the continuum, demonstrating superior curve fitting capability and enabling navigation through narrow, continuously curved spaces.

关键词： Target tracking Limiting Navigation Wires Fitting Kinematics Aerospace electronics

来源：评论

学校读者我要写书评

暂无评论

How the CYBATHLON Competition Has Advanced Assistive Technologies

引用

Control, robotics, and Autonomous Systems 1000年第1期6卷 447-476页

作者： Lukas Jaeger Roberto de Souza Baptista Chiara Basla Patricia Capsi-Morales Yong Kuk Kim Shuro Nakajima Cristina Piazza Michael Sommerhalder Luca Tonin Giacomo Valle Robert Riener Roland Sigrist 1CYBATHLON ETH Zurich Zurich Switzerland email: roland.sigrist@*** 2Laboratory of Robotics and Automation University of Brasilia Brasilia Brazil email: baptista@*** 3Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zurich Zurich Switzerland email: chiara.basla@hest.ethz.chmichael.sommerhalder@hest.ethz.chrobert.riener@hest.ethz.ch 4Department of Informatics Technical University of Munich Garching Germany email: patricia.capsi-morales@tum.decristina.piazza@tum.de 5Laboratory for Movement Biomechanics Institute for Biomechanics Department of Health Sciences and Technology ETH Zurich Zurich Switzerland email: yong.kim@hest.ethz.ch 6Faculty of Systems Engineering Wakayama University Wakayama Japan email: nakajima@wakayama-u.ac.jp 7Department of Information Engineering and Padua Neuroscience Center University of Padua Padua Italy email: luca.tonin@dei.unipd.it 8Neuroengineering Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zurich Zurich Switzerland email: giacomo.valle@hest.ethz.ch 9Balgrist University Hospital Zurich Switzerland

Approximately 1.1. billion people worldwide live with some form of disability, and assistive technology has the potential to increase their overall quality of life. However, the end users’ perspective and needs are often not sufficiently considered during the development of this technology, leading to frustration and nonuse of existing devices. Since its first competition in 2016, CYBATHLON has aimed to drive innovation in the field of assistive technology by motivating teams to involve end users more actively in the development process and to tailor novel devices to their actual daily-life needs. Competition tasks therefore represent unsolved daily-life challenges for people with disabilities and serve the purpose of benchmarking the latest developments from research laboratories and companies from around the world. This review describes each of the competition disciplines, their contributions to assistive technology, and remaining challenges in the user-centered development of this technology.

关键词： user-centered design assistive technology CYBATHLON inclusion people with disabilities daily-life challenge competition

来源：评论

学校读者我要写书评

暂无评论

Indicators to assess physiological heat strain–Part 2: Delphi exercise

引用

Temperature 2022年第3期9卷 263-273页

作者： Ioannou, Leonidas G. Dinas, Petros C. Notley, Sean R. Gofa, Flora Gourzoulidis, George A. Brearley, Matt Epstein, Yoram Havenith, George Sawka, Michael N. Bröde, Peter Mekjavic, Igor B. Kenny, Glen P. Bernard, Thomas E. Nybo, Lars Flouris, Andreas D. FAME Laboratory Department of Physical Education and Sport Science University of Thessaly Trikala Greece Department of Nutrition Exercise and Sports August Krogh Building University of Copenhagen Copenhagen Denmark Human and Environmental Physiology Research Unit Faculty of Health Sciences University of Ottawa Ottawa ON Canada Hellenic National Meteorological Service Athens Greece Hellenic Ministry of Labour and Social Affairs Athens Greece National Critical Care and Trauma Response Centre Royal Darwin Hospital Darwin NT Australia Thermal Hyperformance Pty Ltd Takura Australia Heller Institute of Medical Research Sheba Medical Center Ramat Gan and the Sackler Faculty of Medicine Tel Aviv University Israel Environmental Ergonomics Research Centre Loughborough Design School Loughborough University Loughborough United Kingdom School of Biological Sciences Georgia Institute of Technology Atlanta GA United States Department of Immunology Leibniz Research Centre for Working Environment and Human Factors (IfADO) Dortmund Germany Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa ON Canada College of Public Health University of South Florida Tampa FL United States

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this second paper of the series, we identified the criteria to consider when adopting a TSI to protect individuals who work in the heat, and we weighed their relative importance using a Delphi exercise with 20 experts. Two Delphi iterations were adequate to reach consensus within the expert panel (Cronbach’s α = 0.86) for a set of 17 criteria with varying weights that should be considered when adopting a TSI to protect individuals who work in the heat. These criteria considered physiological parameters such as core/skin/mean body temperature, heart rate, and hydration status, as well as practicality, cost effectiveness, and health guidance issues. The 17 criteria were distributed across three occupational health-and-safety pillars: (i) contribution to improving occupational health (55% of total importance), (ii) mitigation of worker physiological strain (35.5% of total importance), and (iii) cost-effectiveness (9.5% of total importance). Three criteria [(i) relationship of a TSI with core temperature, (ii) having categories indicating the level of heat stress experienced by workers, and (iii) using its heat stress categories to provide recommendations for occupational safety and health] were considered significantly more important when selecting a TSI for protecting individuals who work in the heat, accumulating 37.2 percentage points. These 17 criteria allow the validation and comparison of TSIs that presently exist as well as those that may be developed in the coming years. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Consensus criteria heat indices heat strain hyperthermia labor occupational temperature thermal indices work

来源：评论

学校读者我要写书评

暂无评论

Indicators to assess physiological heat strain–Part 1: Systematic review

引用

Temperature 2022年第3期9卷 227-262页

作者： Ioannou, Leonidas G. Mantzios, Konstantinos Tsoutsoubi, Lydia Notley, Sean R. Dinas, Petros C. Brearley, Matt Epstein, Yoram Havenith, George Sawka, Michael N. Bröde, Peter Mekjavic, Igor B. Kenny, Glen P. Bernard, Thomas E. Nybo, Lars Flouris, Andreas D. FAME Laboratory Department of Physical Education and Sport Science University of Thessaly Trikala Greece Department of Nutrition Exercise and Sports August Krogh Building University of Copenhagen Denmark Human and Environmental Physiology Research Unit Faculty of Health Sciences University of Ottawa ON Canada National Critical Care and Trauma Response Centre Royal Darwin Hospital Northern Territory Darwin Australia Thermal Hyperformance Pty Ltd Takura QLD Australia Heller Institute of Medical Research Sheba Medical Center Ramat Gan and the Sackler Faculty of Medicine Tel Aviv University Israel Environmental Ergonomics Research Centre Loughborough Design School Loughborough University Loughborough United Kingdom School of Biological Sciences Georgia Institute of Technology Atlanta GA United States Department of Immunology Leibniz Research Centre for Working Environment and Human Factors (IfADo) Dortmund Germany Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa Canada College of Public Health University of South Florida 13201 Bruce B Downs Boulevard Tampa 33612 FL United States

In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this first paper of the series, we conducted a systematic review (registration: INPLASY202090088) to identify all TSIs and provide reliable information regarding their use (funded by EU Horizon 2020;HEAT-SHIELD). Eight databases (PubMed, Agricultural and Environmental Science Collection, Web of Science, Scopus, Embase, Russian Science Citation Index, MEDLINE, and Google Scholar) were searched from database inception to 15 April 2020. No restrictions on language or study design were applied. Of the 879 publications identified, 232 records were considered for further analysis. This search identified 340 instruments and indicators developed between 200 BC and 2019 AD. Of these, 153 are nomograms, instruments, and/or require detailed non-meteorological information, while 187 can be mathematically calculated utilizing only meteorological data. Of these meteorology-based TSIs, 127 were developed for people who are physically active, and 61 of those are eligible for use in occupational settings. Information regarding the equation, operating range, interpretation categories, required input data, as well as a free software to calculate all 187 meteorology-based TSIs is provided. The information presented in this systematic review should be adopted by those interested in performing on-site monitoring and/or big data analytics for climate services to ensure appropriate use of the meteorology-based TSIs. Studies two and three in this series of companion papers present guidance on the application and validation of these TSIs, to guide end users of these indicators for more effective use. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： exercise heat indices heat strain hyperthermia labour Occupational temperature thermal indices work

来源：评论

学校读者我要写书评

暂无评论

Hair Direction Detection*

Hair Direction Detection*

引用

WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Commentaries on Viewpoint: Physiology and fast marathons

引用

Journal of applied physiology (Bethesda, Md. : 1985) 2020年第4期128卷 1069-1085页

作者： Jordan Santos-Concejero Fernando González-Mohíno José María González-Ravé Stephane Perrey Arthur H. Dewolf Brandon A. Yates Anton Ušaj Tadej Debevec José Manuel González-Rayas Ana Lilia Rayas-Gómez José Manuel González-Yáñez Romuald Lepers Paul Stapley Julien Louis Felix Proessl P T Nikolaidis B Knechtle D Muniz-Pumares B Hunter L Bottoms Bastien Bontemps Pedro L. Valenzuela Daniel Boullosa Juan Del Coso Richard C. Blagrove Philip R. Hayes Gregoire P. Millet Davide Malatesta Yuri de Almeida Costa Campos Miller Pereira Guimarães Jeferson Macedo Vianna Sandro Fernandes da Silva Paulo Henrique Silva Marques de Azevedo Hunter L. Paris Margaret A. Leist Mast T. Lige William Malysa Alicia S. Oumsang Erin C. Sinai Rasmus K. Hansen Niels H. Secher Stefanos Volianitis Laura Hottenrott Kuno Hottenrott Thomas Gronwald Jonathon W. Senefeld Ricardo J. Fernandes João Paulo Vilas-Boas Alain Riveros-Rivera Dieter Böning Daniel H. Craighead Shalaya Kipp Rodger Kram Christoph Zinner Billy Sperlich Hans-Christer Holmberg Borja Muniz-Pardos Shaun Sutehall Konstantinos Angeloudis Fergus M. Guppy Andrew Bosch Yannis Pitsiladis David C. Andrade Rodrigo Del Rio Rodrigo Ramirez-Campillo Thiago Ribeiro Lopes Bruno Moreira Silva Stephen J. Ives Peter G. Weyand Cayque Brietzke Paulo Estevão Franco-Alvarenga Tony Meireles dos Santos Flávio Oliveira Pires Gwenael Layec Wouter Hoogkamer Christopher S. Balestrini Curtis S. Goss Mikaela C. Gabler Albaro Escalera Shane A. Bielko Robert F. Chapman Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain University of Castilla-La Mancha Sport Training Lab Toledo Spain Facultad de Lenguas y Educación Universidad Nebrija Madrid Spain EuroMov Digital Health in Motion University of Montpellier IMT Mines Ales Montpellier France Department of Systems Medicine and Center of Space Biomedicine University of Rome Tor Vergata Rome Italy Department of Kinesiology Indiana University-Purdue University of Indianapolis Indianapolis Indiana Indiana Center for Musculoskeletal Health Indiana University School of Medicine Indianapolis Indiana Faculty of Sport University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia School of Medicine and Health Sciences Monterrey Institute of Technology and Higher Education Monterrey México Hospital San José de Querétaro Querétaro México INSERM UMR1093-CAPS UFR des Sciences du Sport Université Bourgogne Franche-Comté Dijon France Neural Control of Movement Laboratory School of Medicine Faculty of Science Medicine and Health Illawarra Health and Medical Research Institute University of Wollongong New South Wales Australia Research Institute for Sport and Exercise Sciences Liverpool John Moores University Liverpool United Kingdom Neuromuscular Research Laboratory University of Pittsburgh Pittsburgh Pennsylvania School of Health and Caring Sciences University of West Attica Athens Greece Institute of Primary Care University of Zurich Zurich Switzerland Department of Psychology and Sport Sciences School of Life and Medical Sciences University of Hertfordshire Hatfield United Kingdom Unité de recherche Impact de l’Activité Physique sur la Santé (UR IAPS Number 201723207F) University of Toulon Toulon France Department of Systems Biology University of Alcalá Madrid Spain Department of Sport and Health Spanish Agency for Health Protection in Sport (AEPSAD)

来源：评论

学校读者我要写书评

暂无评论

REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：