ArticleStroke2019
Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.
Abstract readMulticenter Study
In one paragraph
Article in Stroke, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 pooled it
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.World neurosurgery · 2022Pooled it
- The relevance of rich club regions for functional outcome post-stroke is enhanced in women.Human brain mapping · 2023Trial
- Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Automated quantification of the Alberta Stroke Programme Early CT Score (ASPECTS) and infarct core on diffusion-weighted imaging in acute ischaemic stroke: multi-centre validation and severity stratification.Brain communications · 2026Article
- Brain Health Loss Mediates the Effect of Infarct Volume on Functional Outcome in Ischemic Stroke.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.Scientific reports · 2025Article
- Prediction of stroke severity: systematic evaluation of lesion representations.Annals of clinical and translational neurology · 2024Article
- Artificial intelligence in ischemic stroke images: current applications and future directions.Frontiers in neurology · 2024Review
- Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.Brain communications · 2024Article
- Neuroimaging markers of patient-reported outcome measures in acute ischemic stroke.medRxiv : the preprint server for health sciences · 2023Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.Biomedicines · 2023Review
- Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Article
- OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.Frontiers in neurology · 2023Article
- Ischemic stroke subtyping method combining convolutional neural network and radiomics.Journal of X-ray science and technology · 2023Article
- Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome.Neurology · 2022Article
- A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.Scientific data · 2022Article
- Mechanical and Aspiration Thrombectomy in a 2-day-old Neonate with Perinatal Stroke.Clinical neuroradiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
42 authors.
Ona WuFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Stefan WinzeckFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Anne-Katrin GieseDepartment of Neurology, JP Kistler Stroke Research Center, MGH, Boston, MA (A.-K.G., M.R.E., K.D., M.D.S., N.S.R.).
Brandon L HancockFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Mark R EthertonDepartment of Neurology, JP Kistler Stroke Research Center, MGH, Boston, MA (A.-K.G., M.R.E., K.D., M.D.S., N.S.R.).
Mark J R J BoutsFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Kathleen DonahueDepartment of Neurology, JP Kistler Stroke Research Center, MGH, Boston, MA (A.-K.G., M.R.E., K.D., M.D.S., N.S.R.).
Markus D SchirmerDepartment of Neurology, JP Kistler Stroke Research Center, MGH, Boston, MA (A.-K.G., M.R.E., K.D., M.D.S., N.S.R.).
Robert E IrieFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Steven J T MockingFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Elissa C McIntoshFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Raquel BezerraFrom Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown (O.W., S.W., B.L.H., M.J.R.J.B., R.E.I., S.J.T.M., E.C.M., R.B.).
Konstantinos KamnitsasDepartment of Computing, Imperial College London, United Kingdom (K.K.).
Petrea FridDepartment of Clinical Sciences Lund, Lund University, Sweden (P.F., J.W., A.G.L.).
Johan WasseliusDepartment of Clinical Sciences Lund, Lund University, Sweden (P.F., J.W., A.G.L.).
John W ColeDepartment of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD (J.W.C., S.J.K.).
Huichun XuDivision of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (H.X., P.F.M., B.D.M.).
Lukas HolmegaardInstitute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Sweden (L.H.).
Jordi Jiménez-CondeDepartment of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain (J.J.-C., J.R.).
Robin LemmensDepartment of Neurosciences, Experimental Neurology, KU Leuven-University of Leuven (R.L.).
Eric LorentzenDepartment of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Sweden (E.L., T.M.S., C.J.).
Patrick F McArdleDivision of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (H.X., P.F.M., B.D.M.).
James F MeschiaDepartment of Neurology, Mayo Clinic, Jacksonville, FL (J.F.M.).
Jaume RoquerDepartment of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain (J.J.-C., J.R.).
Tatjana RundekDepartment of Neurology, Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL (T.R., R.L.S.).
Ralph L SaccoDepartment of Neurology, Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL (T.R., R.L.S.).
Reinhold SchmidtClinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Austria (R.S.).
Pankaj SharmaInstitute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom (P.S.).
Agnieszka SlowikDepartment of Neurology, Jagiellonian University Medical College, Krakow, Poland (A.S.).
Tara M StanneDepartment of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Sweden (E.L., T.M.S., C.J.).
Vincent ThijsStroke Division, Florey Institute of Neuroscience and Mental Health, HDB, Australia (V.T.).
Achala VagalDepartment of Radiology (A.V.), University of Cincinnati College of Medicine, OH.
Daniel WooDepartment of Neurology and Rehabilitation Medicine (D.W.), University of Cincinnati College of Medicine, OH.
Stephen BevanSchool of Life Science, University of Lincoln, United Kingdom (S.B.).
Steven J KittnerDepartment of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD (J.W.C., S.J.K.).
Braxton D MitchellDivision of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (H.X., P.F.M., B.D.M.).
Jonathan RosandHenry and Allison McCance Center for Brain Health Massachusetts General Hospital, Boston (J.R.).
Bradford B WorrallDepartments of Neurology and Public Health Sciences, University of Virginia, Charlottesville (B.B.W.).
Christina JernDepartment of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Sweden (E.L., T.M.S., C.J.).
Arne G LindgrenDepartment of Clinical Sciences Lund, Lund University, Sweden (P.F., J.W., A.G.L.).
Jane MaguireUniversity of Technology Sydney, Australia (J.M.).
Natalia S RostDepartment of Neurology, JP Kistler Stroke Research Center, MGH, Boston, MA (A.-K.G., M.R.E., K.D., M.D.S., N.S.R.).
Funding
STROKE INCIDENCE AND OUTCOME AMONG BLACKS AND WHITESR01NS030678 · NINDS · UNIVERSITY OF CINCINNATI · PI KISSELA, BRETT M, KLEINDORFER, DAWN O · 1993 to 2024
$26.7MSTROKE INCIDENCE AND RISK FACTORS IN A TRI-ETHNIC REGIONR01NS029993 · NINDS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI ELKIND, MITCHELL S, SACCO, RALPH L. · 1993 to 2019
$19.2MSpecialized Programs of Translational Research in Acute Stroke at the PartnersP50NS051343 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI FURIE, KAREN L · 2006 to 2013
$17.5MResearch BaseP30DK072488 · NIDDK · UNIVERSITY OF MARYLAND BALTIMORE · PI MITCHELL, BRAXTON D, TAYLOR, SIMEON I. · 2005 to 2019
$17.3MProject 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8MThe NINDS International Stroke Genetics Consortium StudyU01NS069208 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI KITTNER, STEVEN J · 2010 to 2013
$9.4MSiblings With Ischemic Stroke Study (SWISS)R01NS039987 · NINDS · MAYO CLINIC JACKSONVILLE · PI MESCHIA, JAMES F · 2000 to 2009
$9.2MICH Recovery GrantR01NS100417 · NINDS · UNIVERSITY OF CINCINNATI · PI WOO, DANIEL · 2017 to 2021
$5.3MISGS: The Ischemic Stroke Genetics StudyR01NS042733 · NINDS · MAYO CLINIC JACKSONVILLE · PI MESCHIA, JAMES F · 2002 to 2006
$3.7MMRI-GENetics Interface Exploration_MRI-GENIE_StudyR01NS086905 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ROST, NATALIA S · 2015 to 2019
$3.3MAssessing Population-based Radiological brain health in Stroke Epidemiology (APRISE) StudyR01NS103824 · NINDS · UNIVERSITY OF CINCINNATI · PI KHATRI, POOJA, KISSELA, BRETT M · 2018 to 2022
$3.1MGenetics of ischemic stroke in the SiGN ConsortiumR01NS100178 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI KITTNER, STEVEN J, MITCHELL, BRAXTON D · 2017 to 2021
$3.0MNCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NIBIB NIH HHS P41 EB015896NIDDK NIH HHS P30 DK072488NINDS NIH HHS K23 NS064052NINDS NIH HHS P50 NS051343NINDS NIH HHS R01 NS029993NINDS NIH HHS R01 NS030678NINDS NIH HHS R01 NS039987NINDS NIH HHS R01 NS042733NINDS NIH HHS R01 NS059775NINDS NIH HHS R01 NS063925NINDS NIH HHS R01 NS082285NINDS NIH HHS R01 NS086905NINDS NIH HHS R01 NS100178NINDS NIH HHS R01 NS100417NINDS NIH HHS R01 NS103824NINDS NIH HHS U01 NS069208NINDS NIH HHS U10 NS077311NINDS NIH HHS U24 NS107222
Abstract
Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm
Indexed as
AdultAgedAged, 80 and overAlgorithmsBig DataBrain IschemiaDiffusion Magnetic Resonance ImagingFemaleHumansImage Processing, Computer-AssistedMachine LearningMaleMiddle AgedNeural Networks, ComputerObserver VariationPhenotypediffusion magnetic resonance imagingmachine learningphenotyperisk factorsstroke
Identifiers
What OpenQuestion holds
Textmetadata
LicenceTDM
Read underepoch 390
Registered trials
None linked
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.