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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

arXiv

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arXiv 2020年

作者： Khodatars, Marjane Shoeibi, Afshin Sadeghi, Delaram Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Zare, Assef Kong, Yinan Khosravi, Abbas Nahavandi, Saeid Hussain, Sadiq Acharya, U. Rajendra Berk, Michael The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran The School of Engineering Macquarie University Sydney2109 Australia The Dibrugarh University Assam786004 India The Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic 599489 Singapore The Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan The Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore The Deakin University IMPACT - the Institute for Mental and Physical Health and Clinical Translation School of Medicine Barwon Health Geelong Australia The Orygen The National Centre of Excellence in Youth Mental Health Centre for Youth Mental Health Florey Institute for Neuroscience and Mental Health The Department of Psychiatry The University of Melbourne Melbourne Australia

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works. © 2020, CC BY-NC-ND.

关键词： Neuroimaging

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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Sadeghi, Delaram Moridian, Parisa Ghassemi, Navid Heras, Jonathan Alizadehsani, Roohallah Khadem, Ali Kong, Yinan Nahavandi, Saeid Zhang, Yu-Dong Gorriz, Juan M. The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Deakin University VIC3217 Australia The School of Engineering Macquarie University Sydney2109 Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Informatics University of Leicester Leicester United Kingdom The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry. University of Cambridge United Kingdom

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1DCNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25. © 2021, CC BY-NC-ND.

关键词： Diseases

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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Techno-economic analysis of MIMO & DAS in 5G

Techno-economic analysis of MIMO & DAS in 5G

引用

Joint IFIP Wireless and Mobile Networking Conference (WMNC)

作者： Christos Bouras Stylianos Kokkalis Anastasia Kollia Andreas Papazois Computer Technology Institute & Press "Diophantus" Patras Greece Computer Engineering & Informatics Dept. University of Patras Greece

2020 will be the year, in which 5G is going to be put into operation, therefore, it is fundamental that all enabling technologies are analyzed in a technical and economic way. The business sector is not very favorable in investing into novel technologies. In this paper, authors propose a techno-economic model for the Multiple Input Multiple Output (MIMO) technology and compare it to the previously developed model for the Distributed Antenna Systems (DAS). Firstly, the experimentation models are analyzed. Secondly, the mathematical models are presented and the specific parameters are opted. Several experiments help comparing and contrasting the two different technologies. There are not many works concerning the techno-economic perspectives of MIMO comparing it with the DAS, this research contributes in this direction.

关键词： MIMO communication Antennas 5G mobile communication Mathematical model Buildings Analytical models Investment

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