Publication:
The RSNA cervical spine fracture CT dataset

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dc.contributor.coauthorLin, Hui Ming
dc.contributor.coauthorColak, Errol
dc.contributor.coauthorRichards, Tyler
dc.contributor.coauthorKitamura, Felipe C.
dc.contributor.coauthorPrevedello, Luciano M.
dc.contributor.coauthorTalbott, Jason
dc.contributor.coauthorBall, Robyn L.
dc.contributor.coauthorGumeler, Ekim
dc.contributor.coauthorYeom, Kristen W.
dc.contributor.coauthorHamghalam, Mohammad
dc.contributor.coauthorSimpson, Amber L.
dc.contributor.coauthorStrika, Jasna
dc.contributor.coauthorBulja, Deniz
dc.contributor.coauthorAngkurawaranon, Salita
dc.contributor.coauthorPérez-Lara, Almudena
dc.contributor.coauthorGómez-Alonso, María Isabel
dc.contributor.coauthorJiménez, Johanna Ortiz
dc.contributor.coauthorPeoples, Jacob J.
dc.contributor.coauthorLaw, Meng
dc.contributor.coauthorYoussef, Ayda
dc.contributor.coauthorMahfouz, Yasser
dc.contributor.coauthorKalpathy-Cramer, Jayashree
dc.contributor.coauthorFlanders, Adam E.
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorAltınmakas, Emre
dc.contributor.kuauthorDoğan, Hakan
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2025-01-19T10:30:18Z
dc.date.issued2023
dc.description.abstractThis dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/. Key Points This is, to our knowledge, the largest publicly available adult cervical spine fracture CT dataset, with contributions from 12 institutions across nine countries and six continents. This dataset includes medical images, segmentations, and expert annotations from a large cohort of radiologists with subspecialist expertise in spine imaging. This dataset was used successfully for the Radiological Society of North America 2022 Cervical Spine Fracture Detection competition hosted on the Kaggle machine learning platform. The dataset is made freely available to the research community for noncommercial use.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue5
dc.description.openaccessAll Open Access; Green Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipFunding text 1: Machine Learning Steering Committee member and the Society for Imaging Informatics in Medicine Machine Learning Education Subcommittee (both unpaid). L.M.P. Associate editor for Radiology: Artificial Intelligence; patents planned, issued, or pending: US-20220051060-A1, “Methods for creating privacy-protecting synthetic data leveraging a constrained generative ensemble model,” and US-20220051402-A1, “Systems for automated lesion detection and related methods.” J.T. Provided expert witness deposition for Phillips, Spallas, & Angstadt in October 2022, unrelated to this article. R.L.B. Support from RSNA to author. E.G. No relevant relationships. K.W.Y. No relevant relationships. M.H. No relevant relationships. A.L.S. Author’s lab receives funding from the National Institutes of Health (NIH); member of the advisory board for the National Cancer Institute Imaging Data Commons. J.S. No relevant relationships. D.B. No relevant relationships. S.A. No relevant relationships. A.P.L. No relevant relationships. M.I.G.A. No relevant relationships. J.O.J. No relevant relationships. J.J.P. Author’s lab receives funding from the NIH, which pays this author’s salary. M.L. No relevant relationships. H.D. No relevant relationships. E.A. No relevant relationships. A.Y. No relevant relationships. Y.M. No relevant relationships. J.K.C. Grants or contracts from GE HealthCare and Genentech; technology licensed to Boston AI; consulting fees from Siloam Vision; deputy editor of Radiology: Artificial Intelligence. A.E.F. Standing director, liaison for information technology, of RSNA board of directors; member of RSNA News editorial board.; Funding text 2: E.C. supported by the Odette Professorship in Artificial Intelligence for Medical Imaging, St Michael’s Hospital, Unity Health Toronto. M.H., A.L.S., and J.J.P. supported in part by the National Institutes of Health (grant R01 CA233888).
dc.description.volume5
dc.identifier.doi10.1148/ryai.230034
dc.identifier.issn2638-6100
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85175041454
dc.identifier.urihttps://doi.org/10.1148/ryai.230034
dc.identifier.urihttps://hdl.handle.net/20.500.14288/26031
dc.identifier.wos1127923700003
dc.keywordsCT
dc.keywordsDiagnosis
dc.keywordsFeature detection
dc.keywordsHead/Neck
dc.keywordsInformatics
dc.keywordsSegmentation
dc.keywordsSpine
dc.language.isoeng
dc.publisherRadiological Society of North America Inc.
dc.relation.grantnoSt Michael’s Hospital; Unity Health Toronto; National Institutes of Health, NIH, (R01 CA233888)
dc.relation.ispartofRadiology: Artificial Intelligence
dc.subjectComputer science, artificial intelligence
dc.subjectRadiology, nuclear medicine and medical imaging
dc.titleThe RSNA cervical spine fracture CT dataset
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorDoğan, Hakan
local.contributor.kuauthorAltınmakas, Emre
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2School of Medicine
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relation.isParentOrgUnitOfPublication17f2dc8e-6e54-4fa8-b5e0-d6415123a93e
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