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The association between bone density of lumbar spines and different daily protein intake in different renal function

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Renal Failure 2024年第1期46卷 2298080页

作者： Lee, Chia-Lin Chen, Kun-Hui Liu, Wei‑Ju Chen, Ching-Hsien Tsai, Shang-Feng Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Intelligent data mining laboratory Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health College of Public Health China Medical University Taichung Taiwan Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung Taiwan Divisions of Nephrology and Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of California at Davis Davis CA United States Division of Nephrology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Life Science Tunghai University Taichung Taiwan

Background: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. Methods: We studied the database of the National Health and Nutrition Examination Survey, 2005–2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8–1.0, 1.0–1.2, and >1.2 g/kg/day). Results: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8–1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062–1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057–1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015–1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051–1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098–1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8–1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. Conclusions: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bone density chronic kidney disease lumbar spine osteoporosis protein diet

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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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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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Recent advances and applications of deep learning methods in materials science

arXiv

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arXiv 2021年

作者： Choudhary, Kamal DeCost, Brian Chen, Chi Jain, Anubhav Tavazza, Francesca Cohn, Ryan WooPark, Cheol Choudhary, Alok Agrawal, Ankit Billinge, Simon J.L. Holm, Elizabeth Ong, Shyue Ping Wolverton, Chris Materials Science and Engineering Division National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research San diegoCA92037 United States Material Measurement Science Division National Institute of Standards and Technology GaithersburgMD20899 United States Department of NanoEngineering University of California San Diego CA92093 United States Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied Sciences Columbia University New YorkNY10027 United States

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design. © 2021, CC BY.

关键词： Application programs

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Application of big data analytics to support power networks and their transition towards smart grids

Application of big data analytics to support power networks ...

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IEEE International Conference on Big data

作者： Sylvie Koziel Patrik Hilber Ryutaro Ichise School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden Informatics Research Division National Institute of Informatics (NII) Tokyo Japan

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Power systems are becoming more complex, which increases instability issues and outage risks. The development of smart grids could help manage such complex systems. One important pillar in smart grids is big data analytics. In this poster paper, we discuss where and how machine learning could contribute to more efficient asset management. We also identify challenges that stand in the way of the widespread use of big data analytics in smart grids. While the nature of data, as well as data and asset management systems themselves make the use of big data challenging, data analytics could improve the reliability of power supply by providing the functions of detection, prediction, and selection.

关键词： Big data Asset management Machine learning Smart grids Prediction algorithms Maintenance engineering

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Towards Clinical AI Fairness: A Translational Perspective

arXiv

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arXiv 2023年

作者： Liu, Mingxuan Ning, Yilin Teixayavong, Salinelat Mertens, Mayli Xu, Jie Ting, Daniel Shu Wei Cheng, Lionel Tim-Ee Ong, Jasmine Chiat Ling Teo, Zhen Ling Tan, Ting Fang Narrendar, Ravi Chandran Wang, Fei Celi, Leo Anthony Ong, Marcus Eng Hock Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Philosophy University of Antwerp Antwerp Belgium Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL United States Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore SingHealth AI Office Singapore Health Services Singapore Singapore Department of Diagnostic Radiology Singapore General Hospital Singapore Singapore Department of Pharmacy Singapore General Hospital Singapore Singapore Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness’ translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness. © 2023, CC BY.

关键词： Health care

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Using Electronic Health Record Mortality data to Promote Goals-of-Care Discussions in Seriously Ill Transferred Patients: A Pilot Study

ACI Open

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ACI Open 2024年第2期8卷 e69-e78页

作者： Neetu Mahendraker Esmeralda Gutierrez-Asis Seho Park Linda S. Williams Titus Schleyer Elizabeth E. Umberfield Department of Medicine Division of General Internal Medicine and Geriatrics Indiana University School of Medicine Indianapolis Indiana United States Indiana University Health Physicians Inc. Indianapolis Indiana United States Regenstrief Institute Indianapolis Indiana United States Department of Industrial and Data Engineering Hongik University Seoul South Korea Roudebush VA Medical Center Health Services Research and Development Indianapolis Indiana United States Department of Neurology Indiana University School of Medicine Indianapolis Indiana United States Department of Nursing Division of Nursing Research Mayo Clinic Rochester Minnesota United States Department of Artificial Intelligence & Informatics Mayo Clinic Rochester Minnesota United States

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