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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Application of a genetic algorithm in triangulation of a 3-D object surface

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International Journal of computers and Applications 2000年第2期22卷 73-77页

作者： Zhenyu, Chen Mbede, J.B. Yan, Zhou Dehua, Li Hanping, Hu State Commn. Res. Open Lab. Image P. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Intelligent Contr. and Robotics Lab. Dept. of Contr. Sci. and Engineering Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Second Artillery Institute Huazhong Univ. of Sci. and Technol. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Second Artillery Institute China Wuhan University China Artificial Intelligence Department University of Edinburgh Inst. of AI and Pattern Recognition Huazhong Univ. of Sci. and Technol. China Cogn. Sci. Natl. Key Found. Res. P. Douala University Cameroon Tübingen University Germany Automatic Control Laboratory Darmstadt University Department of Service Cameroon Min. of National Education Robotics Laboratory Huazhong Univ. of Sci. and Technol. Wuhan China Assoc. Connectionnistes These-ACTH Rennes France IEEE Soc. of Robotics and Automation IEEE Society of Control Systems IASTED Assoc. Jeunes Chercheurs Robotique France Navy University of Engineering China Huazhong Univ. of Sci. and Technol. China Department of Computer Science Airforce Radar Acad. of Engineering China Inst. of P.R and AI Huazhong Univ. of Sci. and Technol.

This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.

关键词： Object recognition

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Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

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BIOLOGICAL PSYCHIATRY 2021年第8期90卷 529-539页

作者： Qi, Shile Schumann, Gunter Bustillo, Juan Turner, Jessica A. Jiang, Rongtao Zhi, Dongmei Fu, Zening Mayer, Andrew R. Vergara, Victor M. Silva, Rogers F. Iraji, Armin Chen, Jiayu Damaraju, Eswar Ma, Xiaohong Yang, Xiao Stevens, Michael Mathalon, Daniel H. Ford, Judith M. Voyvodic, James Mueller, Bryon A. Belger, Aysenil Potkin, Steven G. Preda, Adrian Zhuo, Chuanjun Xu, Yong Chu, Congying Banaschewski, Tobias Barker, Gareth J. Bokde, Arun L. W. Quinlan, Erin Burke Desrivieres, Sylvane Flor, Herta Grigis, Antoine Garavan, Hugh Gowland, Penny Heinz, Andreas Martinot, Jean-Luc Martinot, Marie-Laure Paillere Artiges, Eric Nees, Frauke Orfanos, Dimitri Papadopoulos Paus, Tomas Poustka, Luise Hohmann, Sarah Frohner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Calhoun, Vince D. Sui, Jing [a]Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute Technology and Emory University Atlanta Georgia [b]Department of Psychiatry University of New Mexico Albuquerque New Mexico [c]Department of Psychology Georgia State University Atlanta Georgia [d]Olin Neuropsychiatry Research Center Hartford Connecticut [e]Department of Psychiatry University of California San Francisco San Francisco California [f]Department of Radiology Duke University Durham North Carolina [g]Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina [h]Department of Psychiatry University of Minnesota Minneapolis Minnesota [i]Department of Psychiatry University of California Irvine Irvine California [j]Departments of Psychiatry and Psychology University of Vermont Burlington Vermont [k]Centre for Population Neuroscience and Stratified Medicine Institute for Science and Technology of Brain-Inspired Intelligence Fudan University Shanghai China [l]University of Chinese Academy of Sciences Beijing China [dd]Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China [m]Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China [ee]State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China [n]Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China [o]Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory Nankai University Affiliated Anding Hospital Tianjin China [p]Department of Humanities and Social Science Shanxi Medical University Taiyuan China [q]Department of Psychiatry and Psychotherapy Charité – Universitätsmedizin Berlin Campus Charité Mitte corporate member of Freie Universität Berlin Humboldt-Univers

BACKGROUND: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n z 1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS- associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging bi

关键词： ADHD Attention-deficit/hyperactivity disorder Major depressive disorders MDD Novelty seeking Reward processing Schizophrenia Substance use

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Overview of the recent experimental research on the J-TEXT tokamak

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Nuclear Fusion 2024年第11期64卷 112005-112005页

作者： Ding, Yonghua Wang, Nengchao Chen, Zhongyong Xia, Donghui Yang, Zhoujun Chen, Zhipeng Zheng, Wei Yan, Wei Li, Da Zhou, Song Xu, Xin Zhang, Xiaoyi Mao, Feiyue Fang, Jiangang Ren, Zhengkang Chen, Xixuan Zhang, Junli Zhang, Xiaobo He, Ying Zhang, Qi Li, You Bai, Wei Mou, Lingke Li, Feng Long, Ting Ke, Rui Gao, Li Shi, Peng Shen, Chengshuo Liu, Jiaxing Guo, Weixin Wang, Lu Li, Hanhui Jiang, Zhonghe Zhang, Xiaoqing Li, Jianchao Rao, Bo Cheng, Zhifeng Zhu, Ping Liu, Minghai Xu, Tao Ma, Shaoxiang Yang, Yong Li, Chuan Wang, Zhijiang Zhang, Ming Yu, Kexun Hu, Xiwei Liang, Y. Yu, Q. Gentle, K.W. Pan, Yuan Gentle, Kenneth William Yu, Qingquan Liang, Yunfeng Jiang, Wei Yi, Lin Zhang, Ya Hu, Qiming Li, Jianchao Huang, Mingxiang Li, Haolong Huang, Zhuo Huang, Jie Wang, Weijun He, Mei Zhao, Qing Xu, Jiayu Zhou, Gangyi Ba, Weigang Yang, Cheng Yao, Junjie Wang, Chuliang He, Bo Hu, Yongzhen Deng, Mingyang Fu, Jingwei Liang, Meiling Guan, Yehong Wu, Hongyan Zhuang, Ge Ding, Weixing Lan, Tao Zeng, Shiyong Hussain, Malik Sadam Lin, Zhifang Liu, Hai Sun, Youwen Zeng, Long Liu, Shaocheng Liao, Liang Zhong, Wulyu Shi, Zhongbing Jiang, Min Dong, Yunbo Xu, Min Wang, Aike Zhang, Xiaolong Liu, Linzi Tong, Ruihai Cheng, Jun Nie, Lin Li, Hui Xu, Jianqiang Yang, Zengchen Wen, Jie Liang, Anshu Zhang, Yipo Wu, Yifan Chen, Chengyuan Zhao, Kaijun Shi, Yuejiang Huang, He Austin, Max Lee, Sanggon Luhmann, Neville C. Domier, Calvin W. Zhu, Yilun Brower, David L. Chen, Jie Ida, K. Takemura, Y. Suzuki, Y. Nagasaki, K. Drews, P. Knieps, A. Han, Xiang Krämer-Flecken, A. Henkel, M. He, Jiyang Zhou, Yinan Jia, Ruo Peng, Yue Wang, Pengyu Li, Jiawei Yang, Jie Lu, Conghui Lu, Yiwei Wang, Rumeng Zhang, Hongqi Zeng, Yingqin Zhang, Guodong Ma, Fangyuan Bala, Abba Alhaji Li, Dingchen Hua, Jiankun Ai, Xinkun Tao, Qinlin Zhong, Yu Li, Yangbo He, Xiangrui Wan, Sui Huang, Shanni Ma, Yiming Qu, Zitong Yu, Shimin Huang, Zhangsheng Lei, Chi School of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Southwestern Institute of Physics Chengdu China Institute of Plasma Physics Chinese Academy of Sciences Hefei China Guizhou University of Commerce Guiyang China Wuhan Institute of Technology Wuhan China ITER Organization Route de Vinon-sur-Verdon CS 90 046 Saint-Paul-lez-DuranceCedex 13067 France Forschungszentrum Jülich GmbH IEK-Plasmaphysik Jülich Germany Max-Planck-Institut für Plasmaphysik Garching Germany Institute of Fusion Studies University of Texas at Austin AustinTX United States University of Texas at Austin AustinTX78712 United States School of Physics Huazhong University of Science and Technology Wuhan430074 China Department of Physics Wuhan University of Technology Wuhan430070 China Princeton Plasma Physics Laboratory PO Box 451 PrincetonNJ08543 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China College of Computer Science South-Central University for Nationalities Wuhan430074 China Shenzhen University Shenzhen518060 China University of Science and Technology of China Hefei230026 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China Southwest Jiaotong University Chengdu China College of Nuclear Science and Engineer East China University of Technology PO Box Nanchang330013 China ENN Science and Technology Development Co. Ltd Hebei Key Laboratory of Compact Fusion Langfang065001 China National Fusion Research Institute Daejeon305-333 Korea Republic of University of California DavisCA95616 United States University of California Los Angeles Los AngelesCA90095 United States National Institute for Fusion Science Toki509-5292 Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8527 Japan Institute of Advanced Energy Kyoto University Gokasho Ky

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved;the tearing mode is suppressed;the toroidal injection of 300 kW ECW drives around 24 kA current;fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase. © 2024 The Author(s).

关键词： Tokamak devices

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Author Correction: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

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NPJ digital medicine 2022年第1期5卷 38页

作者： Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S C Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M Zangwill Alejandro F Frangi Tin Aung Ching-Yu Cheng Yu Qiao Xiulan Zhang Daniel S W Ting State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangzhou People's Republic of China. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sciences Shenzhen People's Republic of China. University of Chinese Academy of Sciences Beijing People's Republic of China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. gxtykyy@***. Handan City Eye Hospital Handan People's Republic of China. C-MER (Shenzhen) Dennis Lam Eye Hospital International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen) Shenzhen People's Republic of China. The Eye Hospital WMU at Hangzhou Hangzhou People's Republic of China. Department of Ophthalmology The Second Hospital of Jilin University Changchun People's Republic of China. SenseTime Group Limited Hong Kong People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Guizhou Medical University Kaili People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an People's Republic of China. Department of Ophthalmology The Third Affiliated Hospital of Nanchang University Nanchang People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology UC San Diego La Jolla CA United States. CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds UK. Singapore Eye Research Institute and Singapore National Eye Centre Singapore Singapore. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sci

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Correction to: Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

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Journal of translational medicine 2021年第1期19卷 203页

作者： Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuying Wang Lisheng Wang Xun Xu Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. National Clinical Research Center for Eye Diseases Shanghai 20080 China. Biomedical and Multimedia Information Technology Research Group School of Computer Science The University of Sydney Sydney NSW 2006 Australia. Department of Molecular Imaging Royal Prince Alfred Hospital and the University of Sydney Sydney Australia. Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. lswang@***. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. drxuxun@***. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. drxuxun@***. National Clinical Research Center for Eye Diseases Shanghai 20080 China. drxuxun@***.

An amendment to this paper has been published and can be accessed via the original article.

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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