Academic Journal
CRCS-K Imaging Repository: Architecture, Data Characteristics, and a Proof-of-Concept Analysis of Pretreatment Imaging Workflow.
| Τίτλος: | CRCS-K Imaging Repository: Architecture, Data Characteristics, and a Proof-of-Concept Analysis of Pretreatment Imaging Workflow. |
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| Συγγραφείς: | Kim BJ; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea.; Department of Neurology Seoul National University College of Medicine Seoul Korea., Ryu WS; Artificial Intelligence Research Center JLK Inc. Seoul Korea., Lee MJ; Artificial Intelligence Research Center JLK Inc. Seoul Korea., Kang K; Department of Neurology, Nowon Eulji Medical Center Eulji University School of Medicine Seoul Korea., Kim JG; Department of Neurology, School of Medicine, Daejeon Eulji Medical Center Eulji University Daejeon Korea., Lee SJ; Department of Neurology, School of Medicine, Daejeon Eulji Medical Center Eulji University Daejeon Korea., Cha JK; Department of Neurology Dong-A University Hospital Busan Korea., Park TH; Department of Neurology Seoul Medical Center Seoul Korea., Lee JY; Department of Neurology Soonchunhyang University Seoul Hospital Seoul Korea., Lee K; Department of Neurology Soonchunhyang University Seoul Hospital Seoul Korea., Kwon DH; Department of Neurology Yeungnam University Medical Center Daegu Korea., Lee J; Department of Neurology Yeungnam University Medical Center Daegu Korea., Park HK; Department of Neurology Inje University Ilsan Paik Hospital, Inje University College of Medicine Goyang Korea., Cho YJ; Department of Neurology Inje University Ilsan Paik Hospital, Inje University College of Medicine Goyang Korea., Hong KS; Department of Neurology Inje University Ilsan Paik Hospital, Inje University College of Medicine Goyang Korea., Lee M; Department of Neurology Hallym University Sacred Heart Hospital Anyang Korea., Oh MS; Department of Neurology Hallym University Sacred Heart Hospital Anyang Korea., Yu KH; Department of Neurology Hallym University Sacred Heart Hospital Anyang Korea., Gwak DS; Department of Neurology Dongguk University Ilsan Hospital Goyang Korea., Kim DE; Department of Neurology Dongguk University Ilsan Hospital Goyang Korea., Kim H; Department of Neurology Chonnam National University Medical School, Chonnam National University Hospital Gwangju Korea., Kim JT; Department of Neurology Chonnam National University Medical School, Chonnam National University Hospital Gwangju Korea., Kim JG; Department of Neurology Jeju National University Hospital Jeju City Korea., Choi JC; Department of Neurology Jeju National University Hospital Jeju City Korea., Kim WJ; Department of Neurology Ulsan University Hospital, University of Ulsan College of Medicine Ulsan Korea., Weon YC; Department of Radiology Ulsan University Hospital, University of Ulsan College of Medicine Ulsan Korea., Kwon JH; Department of Neurology Ulsan University Hospital, University of Ulsan College of Medicine Ulsan Korea., Yum KS; Chungbuk National University Hospital Chungbuk National University College of Medicine Cheongju Korea., Shin DI; Chungbuk National University Hospital Chungbuk National University College of Medicine Cheongju Korea., Hong JH; Department of Neurology Keimyung University Dongsan Hospital, Keimyung University School of Medicine Daegu Korea., Sohn SI; Department of Neurology Keimyung University Dongsan Hospital, Keimyung University School of Medicine Daegu Korea., Lee SH; Department of Neurology Hallym University Chuncheon Sacred Heart Hospital Chuncheon-si Gangwon-do Korea., Kim C; Department of Neurology Hallym University Chuncheon Sacred Heart Hospital Chuncheon-si Gangwon-do Korea., Jeong HB; Department of Neurology Chung-Ang University Hospital Seoul Korea., Park KY; Department of Neurology Chung-Ang University Hospital Seoul Korea., Kim CK; Department of Neurology Korea University Guro Hospital Seoul Korea., Kang J; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Kim JY; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Kim DY; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Kim J; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Kim N; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Sunwoo L; Department of Radiology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Baik SH; Department of Radiology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea., Menon BK; Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada., Lin L; Department of Neurology University of New South Wales South Western Sydney Clinical School Liverpool NSW Australia., Parsons M; Department of Neurology University of New South Wales South Western Sydney Clinical School Liverpool NSW Australia., Bae HJ; Department of Neurology Seoul National University Bundang Hospital Seongnam-si Gyeonggi-do Korea.; Department of Neurology Seoul National University College of Medicine Seoul Korea. |
| Πηγή: | Journal of the American Heart Association [J Am Heart Assoc] 2026 Oct 06; Vol. 15 (19), pp. e050940. Date of Electronic Publication: 2026 Sep 24. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101580524 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-9980 (Electronic) Linking ISSN: 20479980 NLM ISO Abbreviation: J Am Heart Assoc Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Wiley-Blackwell |
| Ιατρικοί όροι (MeSH): | Ischemic Stroke*/therapy , Ischemic Stroke*/diagnostic imaging , Neuroimaging*/methods , Registries* , Workflow*, Republic of Korea/epidemiology ; Aged ; Female ; Humans ; Male ; Artificial Intelligence ; Cerebral Angiography ; Endovascular Procedures ; Magnetic Resonance Imaging ; Proof of Concept Study ; Stroke ; Thrombolytic Therapy ; Tomography, X-Ray Computed |
| Περίληψη: | Background: Stroke registries have advanced cerebrovascular research but usually reduce neuroimaging to categorical variables, losing multidimensional information. We describe the CRCS-K (Clinical Research Collaboration for Stroke in Korea) Imaging Repository, a multicenter platform integrating stroke imaging, artificial intelligence-based quantification, and clinical and outcome data through a dedicated research platform, Artificial Intelligence Powered Stroke Clinical-Image Archive Network (AISCAN). Methods: Building upon the nationwide CRCS-K registry, the Imaging Repository collected all computed tomography, magnetic resonance, and angiographic studies obtained during index hospitalization from consecutive patients with acute ischemic stroke at 18 stroke centers. Images underwent centralized deidentification, quality verification, sequence classification, and artificial intelligence-based quantification. As a proof-of-concept application, we examined associations of pretreatment imaging modality with treatment workflow and functional outcomes after intravenous thrombolysis or endovascular treatment. Results: From June 2022 through May 2025, 225 159 imaging sequences were collected from 20 792 patients. AI modules generated standardized numeric features including ischemic lesion volumes, perfusion parameters, white matter hyperintensity burden, and cerebral microbleed counts. Magnetic resonance-first workflows varied substantially across centers, from 1.0% to 56.7%. Greater pretreatment imaging sequence burden was associated with longer door-to-treatment times after intravenous thrombolysis and endovascular treatment. In overlap-weighted analyses, magnetic resonance-based versus computed tomography-based imaging was associated with directionally lower, but not statistically significant, odds of a favorable 3-month outcome after intravenous thrombolysis (odds ratio [OR], 0.90 [95% CI, 0.70-1.18]) and endovascular treatment (OR, 0.89 [95% CI, 0.65-1.21]). Conclusions: Prospective, sequence-level retention of all stroke neuroimaging is feasible at network scale and can be integrated with artificial intelligence-derived features and clinical outcomes. This resource enables real-world investigation of imaging workflows and outcomes. |
| Contributed Indexing: | Keywords: acute ischemic stroke; artificial intelligence; neuroimaging; recanalization treatment; registry |
| Entry Date(s): | Date Created: 20260925 Date Completed: 20261008 Latest Revision: 20261013 |
| Update Code: | 20261014 |
| DOI: | 10.1161/JAHA.126.050940 |
| PMID: | 42786623 |
| Βάση Δεδομένων: | MEDLINE |
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