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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： Cloud computing Privacy Smart cities Pipelines Semantics Robot sensing systems Software

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Differentiator-based disturbance observer 8

Differentiator-based disturbance observer

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8th IEEE Data Driven control and Learning Systems Conference, DDCLS 2019

作者： Li, Xiangyang Gao, Zhiqiang Ai, Wei Tian, Senping School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Center for Advanced Control Technologies Cleveland State University Department of Electrical and Computer Engineering ClevelandOH44115 United States

ISBN: (纸本)9781728114545

The analysis of nonlinear disturbance observer (NDO) is first given in detail and an equivalent differentiator structure is used to explain the NDO. The design procedure of NDO is given according to this equivalent structure. Then the structure and principle of the generalized differentiator-based disturbance observer (DBDO) is given and compared with NDO in their structure and algorithm. As one of DBDO, tracking differentiator (TD) based disturbance observer is applied to compare NDO in simulations. Simulations for disturbance estimation show that both NDO and TD based disturbance observer have very effective disturbance estimation performance but the later has less design restriction. © 2019 IEEE.

关键词： Nonlinear disturbance observer (NDO) Differentiator-based disturbance observer (DBDO) Tracking differentiator (TD) Disturbance observer-based control (DOBC) NDO disturbance observer design process DIFFERENTIATORS

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

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

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

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

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Q-functions, synchronization, and Arnold tongues for coupled stochastic oscillators

arXiv

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arXiv 2025年

作者： Kreider, Max Lindner, Benjamin Thomas, Peter J. Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Bernstein Center for Computational Neuroscience Berlin Berlin10115 Germany Department of Physics Humboldt Universität zu Berlin BerlinD-12489 Germany Department of Mathematics Applied Mathematics and Statistics Department of Biology Department of Dataand Computer Science Department of Electrical Control and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

Phase reduction is an effective theoretical and numerical tool for studying synchronization of coupled deterministic oscillators. Stochastic oscillators require new definitions of asymptotic phase. The Q-function, i.e. the slowest decaying complex mode of the stochastic Koopman operator (SKO), was proposed as a means of phase reduction for stochastic oscillators. In this paper, we show that the Q-function approach also leads to a novel definition of "synchronization" for coupled stochastic oscillators. A system of coupled oscillators in the synchronous regime may be viewed as a single (higher-dimensional) oscillator. Therefore, we investigate the relation between the Q-functions of the uncoupled oscillators and the higher-dimensional Q-function for the coupled system. We propose a definition of synchronization between coupled stochastic oscillators in terms of the eigenvalue spectrum of Kolmogorov’s backward operator (the generator of the Markov process, or the SKO) of the higher dimensional coupled system. We observe a novel type of bifurcation reflecting (i) the relationship between the leading eigenvalues of the SKO for the coupled system and (ii) qualitative changes in the cross-spectral density of the coupled oscillators. Using our proposed definition, we observe synchronization domains for symmetrically-coupled stochastic oscillators that are analogous to Arnold tongues for coupled deterministic oscillators. Copyright © 2025, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction

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Internet Technology Letters 2025年第4期8卷

作者： Shantha Mary Joshitta Dukhbhanjan Singh Sagar Gulati Pooja Sapra Romil Jain Diksha Aggarwal Computer Science Jayaraj Annapackiam College for Women (Autonomous) Periyakulam Affiliated to Mother Teresa Women's University Kodaikanal Tamilnadu India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Information Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Quantum University Research Center Quantum University Roorkee Uttarakhand India

5G mmWave technology offers high data rates and bandwidth, but high path loss and environmental changes affect signal quality. Existing models are not suitable for mmWave channels due to their varying nature over time. To overcome these challenges, this research presents an efficient time-dependent channel modeling framework based on a cuttlefish search-inspired efficient support vector machine (CS-ESVM) for predicting channel characteristics in large-scale measurement and RF at specific measurements at LOS and NLOS. The model is proposed to work for measurements at 28 GHz at a substation. The model also combines a prediction model and playback model for accurate channel characteristics key metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), and correlation coefficient (CC), predicting the radio frequency. The proposed CS-ESVM model achieved the lowest RMSE values of 2.510 (LOS corridor), 1.210 (LOS hall), 1.815 (NLOS corridor), and 1.917 (NLOS hall), the lowest MAPE values of 0.009, 0.004, 0.003, and 0.007, and the highest CC values of 0.899, 0.969, 0.921, and 0.985. The findings suggest that CS-ESVM is more effective at predicting the mmWave channel's characteristics than traditional approaches. In conclusion, this ML-based framework improves the projection of 5G mmWave RF channels and provides a stable solution for real-time prediction in future network environments.

关键词： 28-GHz channel 5G cuttlefish search-inspired efficient support vector machine (CS-ESVM) playback model prediction model

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On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic autonomous Systems (SAS)

arXiv

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arXiv 2021年

作者： Wang, Yingxu Karray, Fakhri Kwong, Sam Plataniotis, Konstantinos N. Leung, Henry Hou, Ming Tunstel, Edward Rudas, Imre J. Trajkovic, Ljiljana Kaynak, Okyay Kacprzyk, Janusz Zhou, Mengchu Smith, Michael H. Chen, Philip Patel, Shushma University of Calgary Canada FIEEE Dept. of Electrical & Computer Engineering University of Waterloo ON Canada FIEEE Department of Computer Science City University of Hong Kong Hong Kong FIEEE Dept. of Electrical & Computer Engineering University of Toronto ON Canada SMIEEE Toronto Research Centre DRDC Canada FIEEE Autonomous & Intelligent Systems Dept. Raytheon Technologies Research Center United States Óbuda University Budapest Hungary FIEEE School of Engineering Science Simon Fraser University BurnabyBC Canada FIEEE Bogazici University Bebek Istanbul Turkey FIEEE Systems Research Institute Polish Academy of Sciences Warsaw Poland FIEEE Dept. of Computer Science New Jersey Institute of Technology NJ United States SMIEEE Furaxa Inc. OrindaCA United States FIEEE School of Computer Science and Engineering South China University of Technology Guangzhou China FBCS Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom

Symbiotic autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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Fractional-order biological system: chaos, multistability and coexisting attractors

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The European Physical Journal Special Topics 2021年第5期231卷 1061-1070页

作者： Debbouche, Nadjette Ouannas, Adel Momani, Shaher Cafagna, Donato Pham, Viet-Thanh Laboratory of Dynamical System and Control University of Larbi Ben M’hidi Oum El Bouaghi Algeria Department of Mathematics and Computer Sciences Larbi Ben M’hidi University Oum El Bouaghi Algeria Nonlinear Dynamics Research Center (NDRC) College of Humanities and Sciences Ajman University Ajman UAE Department of Mathematics Faculty of Science University of Jordan Amman Jordan Department of Innovation Engineering University of Salento Lecce Italy Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

In this paper, the nonlinear dynamics of the biological system modeled by the fractional incommensurate order Van der Pol equations are investigated. The stability of the proposed fractional non-autonomous system is analyzed by varying both the fractional order derivative and system parameters. Moreover, very interesting phenomena such as symmetry, multi-stability and coexistence of attractors are discovered in the considered biological system. Numerical simulations are performed by considering the Caputo fractional derivative and results are reported by means of bifurcation diagrams, computation of the largest Lyapunov exponent, phase portraits in 2D and 3D projections.

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Removal notice to “Machine learning in health condition check-up: An approach using Breiman's random forest algorithm”

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Measurement: Sensors 2023年 27卷

作者： Yousef Methkal Abd Algani Mahyudin Ritonga B. Kiran Bala Mohammed Saleh Al Ansari Malek Badr Ahmed I. Taloba Department of Mathematics Sakhnin College Israel Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Muhammadiyah University of West Sumatra Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering (Autonomous) Samayapuram 621112 Trichy India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain The University of Mashreq Research Center Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad 10021 Iraq Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

This article has been removed: please see Elsevier Policy on Article Withdrawal ( https://***/about/policies/article-withdrawal ). This article has been removed at the request of the Authors, due to incomplete authorisation for the publication of the article from one of the author's institutions. The authors sincerely apologize for the inconvenience.

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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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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