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A manually-actuated MRI compatible robotic manipulator for image-guided interventions

Physica Medica 2014年 30卷 e55-e56页

作者： Eftychios G. Christoforou Christoforos Keroglou Andrew Webb Nikolaos V. Tsekos Ioannis Seimenis KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus C.J. Gorter Center for High Field MRI Leiden University Medical Center Leiden Netherlands Medical Robotics Laboratory University of Houston Houston TX USA Med. Physics Lab Medical School Democritus University of Thrace Alexandroupolis Greece

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Healthcare Informatics Society of Ireland

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Irish Journal of Medical Science 1999年第4期168卷 276-282页

作者： Rigby, M. Sheridan, A. Draper, R. Stephens, G. Gaffney, P. Grimson, J. Avalos, G. Comerford, F. R. Kanagaratnam, B. Dineen, B. Hamilton, I. Jung, B. Felton, E. Berry, D. Grimson, W. Rice, D. H. Healy, M. Bruce, A. Kelly, J. Carroll, T. Grogan, A. Hurl, G. A. Hyland, D. Roberts, J. Brazil, J. Callanan, I. Devitt, A. O’Brien, T. O’Sullivan, U. M. Peyton, K. Wilson, M. J. Collins, R. Crawley, L. Foley, D. Strachan, H. McBrearty, P. Murnane, R. Murphy, E. Keane, V. Mulvany, F. Sweeney, P. D. Kenny, D. O’Connell, P. R. Balfe, P. Minogue, G. Centre for Health Planning & Management Keele University Keele Staffordshire Britain St. John of God Hospital Stillorgan Hospitaller Order of St. John of God Stillorgan Dept. of Computer Science Trinity College Dublin Clinical Chemistry Laboratory Tallaght Hospital Tallaght Dublin 24 Department of Experimental Medicine Faculty of Medicine National University of Ireland Galway Department of Health Promotion Faculty of Medicine National University of Ireland Galway School of Control Systems & Electrical Engineering Dublin Institute of Technology Ireland Shared Medical Systems Malvern USA Healy Services Dublin Universal Learning Systems Ltd. Dublin The Rotunda Hospital Dublin Mater Misericordiae Hospital Dublin Institute for Healthcare Informatics Dublin First Consulting Group Macclesfield Britain Virus Reference Laboratory UCD Belfield Dublin 4 Cappagh Orthopaedic Hospital Dublin Ireland Southern Health Board Cork Mater Misericordiae Hospital Dublin 7 DSS Dublin Ltd. Ireland University College Dublin Ireland Mary Aikenhead School of Nursing St. Vincent’s Hospital Dublin Blackrock Clinic Dublin Borders Community Health Services NHS Trust Britain James Connolly Memorial Hospital Blanchardstown Dublin 15 Kildare and West Wicklow Mental Health Service for People with A Learning Disability Eastern Health Board Dublin Health Research Board Dublin General Practice Research Unit The Medical Centre Dungloe Dept. of Clinical Biochemistry Our Lady’s Hospital for Sick Children Crumlin Dublin 12

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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

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GardnerNet: An Interpretable Deep Learning Model for Quantitative Human Blastocyst Evaluation - A Retrospective Model Development and Validation Study

SSRN

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SSRN 2024年

作者： Liu, Hang Chen, Longbin Shan, Guanqiao Sun, Chen Liao, Hongqing Lu, Changfu Zhang, Shuoping Dong, Shaonan Xu, Xinxin Yan, Qiuyun Gong, Fei Zhang, Zhuoran Dai, Changsheng Chen, Wenyuan Song, Haocong Chen, Lei Sun, Haixiang Wang, Shanshan Lin, Ge Sun, Yu Gu, Yifan Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada Institute of Reproductive and Stem Cell Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Hengyang Nanhua-Xinghui Reproductive Health Hospital 30 Jiefang Road Hunan Province Hengyang China The second Affiliated Hospital Hengyang Medical School University of South China 30 Jiefang Road Hunan Province Hengyang City China Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China NHC Key Laboratory of Human Stem Cell and Reproductive Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Clinical Research Center for Reproduction and Genetics in Hunan Province Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen 2001 Longxiang Boulevard Longgang District Shenzhen China Institute of Robotics and Intelligent Systems Dalian University of Technolgy 2 Linggong Road Liaoning Province Dalian China Department of Computer Science University of Toronto 40 St George Street TorontoONM5S 2E4 Canada Center for Reproductive Medicine and Obstetrics and Gynecology Nanjing Drum Tower Hospital Nanjing University Medical School 321 Zhongshan Road Jiangsu Province Nanjing China National Engineering and Research Center of Human Stem Cell 8 Luyun Road Jiangsu Province Changsha China Department of Electrical and Computer Engineering 10 King’s College Road TorontoONM5S 3G8 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Background: Selecting the blastocyst with the highest potential for live birth from a cohort is crucial for the success of in vitro fertilization (IVF). While the Gardner and schoolcraft morphological grading system, commonly known as the Gardner grading system, is widely used by embryologists worldwide, blastocysts having similar or the same inner cell mass (ICM) and trophectoderm (TE) grades cause challenges for embryologists in decision-making. Furthermore, human assessment is subjective and inconsistent in predicting which blastocysts have higher potential to result in live birth. This study aims to develop an interpretable, quantitative method aligned with the Gardner grading system, providing continuous scores that correlate with live birth outcomes, thereby assisting embryologists in improving blastocyst selection. Methods: In this retrospective study, we developed GardnerNet, an interpretable deep learning model that outputs expansion degree grade and continuous scores quantifying a blastocyst's ICM morphology and TE morphology, based on the Gardner grading system. The continuous ICM and TE scores were calculated by weighting each base grade's predicted probability and summing the predicted probabilities. To represent each blastocyst's overall potential for live birth, we combined the ICM and TE scores using their odds ratios (ORs) for live birth. We further assessed the correlation between the ICM score, TE score, and the OR-combined score (adjusted for expansion degree) with live birth rates by applying GardnerNet to blastocysts with known live birth outcomes. To test its generalizability, we also applied GardnerNet to an external IVF institution, accounting for variations in imaging conditions, live birth rates, and embryologists' experience levels. Findings: GardnerNet was developed using data from 2760 blastocysts with majority-voted grades for expansion degree, ICM, and TE. The model achieved mean area under the receiver operating characteristic curve

关键词： Risk assessment

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An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare systems

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Kunyu Xie Lin Zhang Yuan Yang Xiaohe Li Ridha Khedri Zhen Chen M. Jamal Deen Simulation Technology Innovation Center Hangzhou International Innovation Institute of Beihang University Hangzhou 311115 Zhejiang China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenzhen Third People’s Hospital & Second Hospital Affiliated to Southern University of Science and Technology Shenzhen 518112 Guangdong China Dept. of Computing and Software McMaster University Hamilton Ontario Canada L8S 4K1 Department of Electrical and Computer Engineering McMaster University Hamilton ON L8S 4L8 Canada

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based systems engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

关键词： Digital Twin Model-based system engineering X Language Medical Cyber-Physical systems Digital Twin Healthcare Design

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Corrigendum to “Numerical analysis of the magnetic field impact on hydrothermal characteristics of a microchannel heatsink with Fe 3 O 4 ferrofluid and various pin-fin shapes” [J. Magn. Magn. Mater. 585 (2023) 171102]

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Journal of Magnetism and Magnetic Materials 2024年 610卷

作者： Tao Hai Kamal Sharma Riyam K. Marjan Babak Farhang Mohamed H. Mahmoud Hassan Fouad Walid El-Shafai School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China School of Electronics and Information Engineering Ankang University China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 China Institute of Engineering and Technology GLA University Mathura UP 281406 India Department of Intelligent Medical Systems Al-Mustaqbal University College Babylon 51001 Iraq Department of Energy Technology Aalborg University Denmark Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo P.O. Box 11795 Egypt Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers

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Open Forum Infectious Diseases 2017年第SUPPL_1期4卷 S403-S404页

作者： Makar, Maggie Oh, Jeeheh Fusco, Christopher Marchesani, Joseph McCaffrey, Robert Rao, Krishna Ryan, Erin E Washer, Laraine West, Lauren R Young, Vincent B Guttag, John Hooper, David C Shenoy, Erica S Wiens, Jenna Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts Computer Science and Engineering University of Michigan Ann Arbor Michigan Information Systems Partners HealthCare Boston Massachusetts Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Infection Control Unit Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts Department of Medicine Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Medical Practice Evaluation Center Department of Medicine Massachusetts General Hospital Boston Massachusetts

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Author Correction: Frontotemporal coordination predicts working memory performance and its local neural signatures

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Nature communications 2021年第1期12卷 3811页

作者： Ehsan Rezayat Mohammad-Reza A Dehaqani Kelsey Clark Zahra Bahmani Tirin Moore Behrad Noudoost School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. dehaqani@ut.ac.ir. Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran. dehaqani@ut.ac.ir. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. Department of Biomedical Engineering Tarbiat Modares University Tehran Iran. Department of Neurobiology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. behrad.noudoost@utah.edu.

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The 2024 Active Metamaterials Roadmap

arXiv

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arXiv 2024年

作者： Pope, Simon A. Roth, Diane J. Bansal, Aakash Mousa, Mostafa Rezanejad, Ashkan Forte, Antonio E. Nash, Geoff.R. Singleton, Lawrence Langfeldt, Felix Cheer, Jordan Henthorn, Stephen Hooper, Ian R. Hendry, Euan Powell, Alex W. Souslov, Anton Plum, Eric Sun, Kai de Groot, C.H. Muskens, Otto L. Shields, Joe De Galarreta, Carlota Ruiz Wright, C. David Kocabas, Coskun Ergoktas, M. Said Xiao, Jianling Schulz, Sebastian A. Di Falco, Andrea Krasavin, Alexey V. Zayats, Anatoly V. Galiffi, Emanuele Department of Automatic Control and Systems Engineering The University of Sheffield Amy Johnson Building Portobello Street SheffieldS1 3JD United Kingdom Department of Physics London Centre for Nanotechnology King’s College London Strand WC2R 2LS United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Natural Sciences University of Exeter Harrison Building North Park Road Exeter United Kingdom Institute of Sound & Vibration Research University of Southampton University Road Southampton United Kingdom Department of Electronic and Electrical Engineering University of Sheffield Sheffield United Kingdom Centre for Metamaterial Research and Innovation University of Exeter Stoker Road ExeterEX4 4QL United Kingdom T.C.M. Group Cavendish Laboratory University of Cambridge CambridgeCB3 0HE United Kingdom Optoelectronics Research Centre Centre for Photonic Metamaterials University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom School of Physics and Astronomy University of Southampton Southampton United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom Institute of Materials Science of Barcelona Universitat Autònoma de Barcelona Bellaterra Barcelona08193 Spain Department of Materials National Graphene Institute University of Manchester United Kingdom Schoolof Physics and Astronomy University of St Andrews North Haugh Fife St. AndrewsKY16 9SS United Kingdom Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Photonics Initiative Advanced Science Research Centre City University of New York 85 St. Nicholas Terrace New YorkNY10027 United States

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab -based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Microwave acoustics

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