{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:58:42Z","timestamp":1776416322845,"version":"3.51.2"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019486","name":"National Natural Scientific Foundation of China","doi-asserted-by":"publisher","award":["82227807"],"award-info":[{"award-number":["82227807"]}],"id":[{"id":"10.13039\/501100019486","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019486","name":"National Natural Scientific Foundation of China","doi-asserted-by":"publisher","award":["82572180"],"award-info":[{"award-number":["82572180"]}],"id":[{"id":"10.13039\/501100019486","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019978","name":"Research Project of Shanghai Municipal Health Commission","doi-asserted-by":"publisher","award":["2024ZZ1018"],"award-info":[{"award-number":["2024ZZ1018"]}],"id":[{"id":"10.13039\/501100019978","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019978","name":"Research Project of Shanghai Municipal Health Commission","doi-asserted-by":"publisher","award":["2024ZDXK0066"],"award-info":[{"award-number":["2024ZDXK0066"]}],"id":[{"id":"10.13039\/501100019978","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019978","name":"Research Project of Shanghai Municipal Health Commission","doi-asserted-by":"publisher","award":["2022JC017"],"award-info":[{"award-number":["2022JC017"]}],"id":[{"id":"10.13039\/501100019978","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013279","name":"National Key R&D Program of the Ministry of Science and Technology","doi-asserted-by":"publisher","award":["2022YFC2009904"],"award-info":[{"award-number":["2022YFC2009904"]}],"id":[{"id":"10.13039\/501100013279","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The purpose of this study was to develop and validate a deep learning framework for simultaneous automated quantification of diffusion-weighted imaging-the Alberta Stroke Programme early CT score (DWI-ASPECTS) and infarct core volume in middle cerebral artery acute ischaemic stroke (MCA-AIS) and to evaluate its clinical utility for severity stratification in multi-centre settings. A cohort of 738 patients diagnosed with MCA-AIS from four centres was divided into a train set (n = 408), a validation set (n = 116), an internal test set (n = 60) and an external test set (n = 154). A 3D U-Net architecture was trained for simultaneous infarct segmentation and the ASPECTS region analysis. Model performance was compared against expert neuroradiologists using intraclass correlation coefficients and the Spearman correlation coefficient. Furthermore, we investigated the correlation between ASPECTS deduction frequency, ASPECTS score, core volume and the severity of MCA-AIS. The 3D U-Net model showed a high correlation with manual segmentation, achieving a Dice coefficient of 0.801 and a Spearman correlation coefficient of 0.988 for volume measurements in the external test set. In both the internal and external test sets, the automated ASPECTS by the deep learning (DL) model (Automated), manual ASPECTS by raters (Raters) and manual ASPECTS by raters on DWI images registered with the template (Raters_template) exhibited a strong correlation and excellent agreement. The cortical regions (M1\u2013M6) were particularly relevant in patients classified into the moderate\u2013severe group. The threshold values for the mild group and moderate\u2013severe group on receiver operating characteristic curve analysis for DWI-ASPECTS and Core volume, were 6 and 27.86\u2005mL, respectively. The DL model demonstrated comparable performance to neuroradiologists\u2019 evaluation, potentially serving as an ancillary tool for physicians in making urgent clinical decisions. The severity of MCA-AIS was significantly associated with the specific ASPETCS regions and core volume, which may aid in identifying moderate\u2013severe MCA-AIS.<\/jats:p>","DOI":"10.1093\/braincomms\/fcag053","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:24:27Z","timestamp":1771849467000},"source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.1093","volume":"8","author":[{"given":"Lai","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University , Shanghai 200434 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200235 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200235 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200235 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine , Shanghai 200120 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine , Shanghai 200092 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huali","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine , Shanghai 200062 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kangwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University , Shanghai 200065 ,","place":["China"]},{"name":"Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine , Shanghai 200065 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5394-3841","authenticated-orcid":false,"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University , Shanghai 200065 ,","place":["China"]},{"name":"Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine , Shanghai 200065 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Fourth People's Hospital, School of Medicine, Tongji University , Shanghai 200434 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University , Shanghai 200065 ,","place":["China"]},{"name":"Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine , Shanghai 200065 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"issue":"1","key":"2026041703581612100_fcag053-B1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1161\/STROKEAHA.117.019508","article-title":"DWI-ASPECTS (Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores) and DWI-FLAIR (Diffusion-Weighted Imaging-Fluid Attenuated Inversion Recovery) mismatch in thrombectomy candidates: An intrarater and interrater agreement study","volume":"49","author":"Fahed","year":"2018","journal-title":"Stroke"},{"issue":"9","key":"2026041703581612100_fcag053-B2","doi-asserted-by":"crossref","first-page":"e0274562","DOI":"10.1371\/journal.pone.0274562","article-title":"Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia","volume":"17","author":"Joo","year":"2022","journal-title":"PLoS One"},{"issue":"7","key":"2026041703581612100_fcag053-B3","doi-asserted-by":"crossref","first-page":"105814","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.105814","article-title":"ASPECTS-Region-dependent functional outcomes after endovascular therapy in patients with cardioembolic stroke","volume":"30","author":"Oki","year":"2021","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"4","key":"2026041703581612100_fcag053-B4","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s00062-022-01156-z","article-title":"Clinical impact and predictors of diffusion weighted imaging (DWI) reversal in stroke patients with diffusion weighted imaging Alberta stroke program early CT score 0\u20135 treated by thrombectomy: Diffusion weighted imaging reversal in large volume stroke","volume":"32","author":"Panni","year":"2022","journal-title":"Clin Neuroradiol"},{"issue":"1","key":"2026041703581612100_fcag053-B5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1056\/NEJMoa1706442","article-title":"Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct","volume":"378","author":"Nogueira","year":"2018","journal-title":"N Engl J Med"},{"issue":"8","key":"2026041703581612100_fcag053-B6","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1056\/NEJMoa1713973","article-title":"Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging","volume":"378","author":"Albers","year":"2018","journal-title":"N Engl J Med"},{"issue":"11","key":"2026041703581612100_fcag053-B7","first-page":"1273","article-title":"Value of MRI T2 FLAIR vascular hyperintensities combined with DWI ASPECTS in predicting the prognosis of acute cerebral infarction with endovascular 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