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arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

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ANTARCTICA AND THE SOUTHERN OCEAN

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第9期104卷 S322-S365页

作者： Clem, Kyle R. Raphael, Marilyn N. School of Geography Environment and Earth Sciences Victoria University of Wellington Wellington New Zealand Scripps Institution of Oceanography University of California San Diego La Jolla California Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado Earth Science Observation Center Cooperative Institute for Research in Environmental Sciences (ESOC/CIRES) University of Colorado Boulder Boulder Colorado Argentine Naval Hydrographic Service Buenos Aires Argentina Department of Earth and Environmental Science Temple University Philadelphia Pennsylvania Department of Meteorology University of Valparaíso Valparaíso Chile Center for Climate and Resilience Research (CR)2 Santiago Chile British Antarctic Survey Cambridge United Kingdom Science Systems and Applications Inc. Lanham Maryland NASA Goddard Space Flight Center Greenbelt Maryland Royal Netherlands Meteorological Institute (KNMI) DeBilt The Netherlands Department of Marine Sciences University of Gothenburg Gothenburg Sweden Department of Geography Ohio University Athens Ohio NASA Jet Propulsion Laboratory Pasadena California NOAA/OAR Earth System Research Laboratory Global Monitoring Division Boulder Colorado University of Colorado Boulder Boulder Colorado National Oceanography Centre Southampton United Kingdom. Antarctic Meteorological Research and Data Center Space Science and Engineering Center University of Wisconsin-Madison Madison Wisconsin Department of Physical Sciences School of Arts and Sciences Madison Area Technical College Madison Wisconsin Australian Bureau of Meteorology and Institute for Marine and Antarctic Studies (IMAS) University of Tasmania Hobart Australia Earth Science and Observation Center CIRES University of Colorado Boulder Colorado University of St. Andrews St Andrews United Kingdom Princeton University Princeton New Jersey Australian Antarctic Division Australian Antarctic Program Partnership (AAPP) and Australian Centre fo

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Deep Neural Networks Carve the Brain at its Joints

arXiv

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arXiv 2020年

作者： Bertolero, Maxwell A. Moraczewski, Dustin Thomas, Adam Bassett, Danielle S. Department of Bioengineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Data Sharing and Science Team National Institute of Mental Health BethesdaMD United States Department of Psychiatry Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical & Systems Engineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics & Astronomy College of Arts & Sciences University of Pennsylvania PhiladelphiaPA19104 United States Santa Fe Institute Santa FeNM87501 United States

How an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A proposed approach to reduce the vulnerability in a cloud system

Communications in Computer and Information Science

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Communications in Computer and Information Science 2018年 872卷 55-66页

作者： Saadi, Chaimae Chaoui, Habiba Systems Engineering Laboratory Data Analysis and Security Team National School of Applied Sciences Campus Universitaire B.P 241 Kénitra14000 Morocco

Today, cloud computing is becoming more and more popular as a Pay-as-You-Go model for providing on-demand services over the Internet. In this paper, we will propose new detection and prevention mechanisms for cloud systems to protect against different types of attacks and vulnerabilities by improving a new architecture that provides a security mechanism including a virtual firewall and IDS/IPS (Intrusion Detection and Prevention System) which aims to secure the virtual environment. © Springer Nature Switzerland AG 2018.

关键词： Virtualization

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Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

arXiv

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arXiv 2025年

作者： Zhang, Yanbo Khan, Sumeer A. Mahmud, Adnan Yang, Huck Lavin, Alexander Levin, Michael Frey, Jeremy Dunnmon, Jared Evans, James Bundy, Alan Dzeroski, Saso Tegner, Jesper Zenil, Hector Allen Discovery Center Tufts University MedfordMA United States Living Systems Lab KAUST Thuwal Saudi Arabia Intelligent Infrastructure Team Network Rail United Kingdom Department for AI in Society Science and Technology Zuse Institute Berlin Germany NVIDIA Research United States Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal23952 Saudi Arabia Pasteur Labs BrooklynNY United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Chemistry University of Southampton University Road Hampshire Southampton United Kingdom Department of Biomedical Data Science Stanford University StanfordCA United States Knowledge Lab Department of Sociology University of Chicago IL United States Santa Fe Institute NM United States School of Informatics The University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Thuwal23955-6900 Saudi Arabia Unit of Computational Medicine Department of Medicine Center for Molecular Medicine Karolinska Institutet Karolinska University Hospital L8:05 StockholmSE-171 76 Sweden Thuwal23955-6900 Saudi Arabia Science for Life Laboratory Tomtebodavagen 23A SolnaSE-17165 Sweden Algorithmic Dynamics Lab Research Departments of Biomedical Computing and Digital Twins School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom King’s Institute for Artificial Intelligence London United Kingdom The Alan Turing Institute London United Kingdom Oxford Immune Algorithmics Oxford University Innovation London Institute for Healthcare Engineering London United Kingdom Cancer Interest Group Francis Crick Institute London United Kingdom

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with ‘reasoning’, mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone. Copyright © 2025, The Authors. All rights reserved.

关键词： Plants (botany)

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Prediction of cognitive states during flight simulation using multimodal psychophysiological sensing

Prediction of cognitive states during flight simulation usin...

引用

AIAA Information systems-Infotech At Aerospace Conference, 2017

作者： Harrivel, Angela R. Stephens, Chad L. Milletich, Robert J. Heinich, Christina M. Last, Mary Carolyn Napoli, Nicholas J. Abraham, Nijo A. Prinzel, Lawrence J. Motter, Mark A. Pope, Alan T. Crew Systems and Aviation Operations Branch NASA Langley Research Center Mail Stop 152 HamptonVA23681 United States Comprehensive Digital Transformation Big Data Analytics and Machine Intelligence Team NASA Langley Research Center HamptonVA23681 United States Crew Systems and Aviation Operations Branch Analytical Mechanics Associates Inc. NASA Langley Research Center Mail Stop 152 HamptonVA23681 United States National Institute of Aerospace Systems and Information Engineering University of Virginia NASA Langley Research Center HamptonVA23681 United States Systems Integration and Test Branch NASA Langley Research Center Mail Stop 424 HamptonVA23681 United States Electronic Instrumentation Systems Branch NASA Langley Research Center Mail Stop 488 HamptonVA23681 United States

ISBN: (数字)9781624104497

ISBN: (纸本)9781624104497

The Commercial Aviation Safety team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training

关键词： Forecasting

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SAR Processing by a modified CSA based algorithm with a delicate azimuth matched filter ETF4ZDT 11

SAR Processing by a modified CSA based algorithm with a deli...

引用

11th European Conference on Synthetic Aperture Radar, EUSAR 2016

作者： Dong Hyun, Kim Byoung Gyun, Lim Dong Han, Lee Jae Chul, Yoon Do Chul, Yang Ho Ryung, Jung Cal/Val and Data Quality Control Team Korea Aerospace Research Institute 169-84 Gwahak-ro Yuseong-gu Daejeon305-806 Korea Republic of KOMPSAT-6 Systems Engineering and Integration Team Korea Aerospace Research Institute Korea Republic of

KOMPSAT-6 in constellation of KOMSAT-5 is foreseen to be operating in near future to be used for variety of SAR applications and monitoring of Earth environment. In order to pursue an efficient and reliable product delivery, a genuine processing software concept is devised in which the algorithm is based on the well known chirp scaling algorithm. The efficiency of the algorithm is made through incorporation of a delicate azimuth matched filter named as ETF4ZDT which means exact transfer function 4th order zero Doppler time. The base principle of it is from ETF4 [2] which is adopting 4th order polynomials for range history tracking. A practical format of the enhanced azimuth matched filter is delivered by ETF4ZDT [3] which performs highly accurate compression, focusing targets to their zero Doppler time at once and preserves phases of targets for their closest approach ranges simultaneously without requiring any additional phase compensation. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Matched filters

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What Can Cause Big data Being Slow?

What Can Cause Big Data Being Slow?

引用

SAI Computing Conference

作者： Mohamed Ali Stouky Habiba Chaoui Systems Engineering Laboratory Data Analysis and Security Team National School of Applied Sciences Campus Universitaire

ISBN: (纸本)9781467384612

Companies operating in some areas can gather all the data they need with relative ease, more specifically marketing companies, social networks or internet companies to name but a few. However when it comes to analyzing these data, many problems may occur. Insufficient resources that can store all this flood of data or highly significant response time to get the results. In some definitions of Big data, one of the main characteristics is Velocity as the results should be obtained quickly. This is why optimization is a top priority while working in a Big data environment. In this paper we provide a state-of-the-art report of the main causes of performance related problems in a Big data environment. Our study is mainly focused on Hadoop as it is free and the most popular solution when it comes to Big data.

关键词： Big data Mapreduce Hadoop data layout Indexes Job tuning Job scheduling

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

引用

The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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