{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:14:14Z","timestamp":1761174854701,"version":"build-2065373602"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003841","name":"Ilam University of Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003841","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroradiology"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00234-025-03745-4","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T05:35:30Z","timestamp":1757655330000},"page":"2267-2281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The best diagnostic approach for classifying ischemic stroke onset time: A systematic review and meta-analysis"],"prefix":"10.1007","volume":"67","author":[{"given":"Seyed Salman","family":"Zakariaee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dler Hussein","family":"Kadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikaeil","family":"Molazadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahab","family":"Abdi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"3745_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag","volume":"90","author":"H Zhang","year":"2021","unstructured":"Zhang H, Polson JS, Nael K et al (2021) Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time. Comput Med Imaging Graph 90:1\u201310. https:\/\/doi.org\/10.1016\/j.compmedimag","journal-title":"Comput Med Imaging Graph"},{"key":"3745_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.nicl.103544","volume":"40","author":"EMZ Akay","year":"2023","unstructured":"Akay EMZ, Rieger J, Sch\u00f6ttler R et al (2023) A deep learning analysis of stroke onset time prediction and comparison to DWI-FLAIR mismatch. NeuroImage Clin 40:1\u20139. https:\/\/doi.org\/10.1016\/j.nicl.103544","journal-title":"NeuroImage Clin"},{"issue":"1","key":"3745_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3934\/mbe.2024002","volume":"21","author":"J Lu","year":"2024","unstructured":"Lu J, Guo Y, Wang M et al (2024) Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain. Math Biosci Eng 21(1):34\u201348. https:\/\/doi.org\/10.3934\/mbe.2024002","journal-title":"Math Biosci Eng"},{"key":"3745_CR4","unstructured":"Ho KC, Speier W, El-Saden S, Arnold CW (eds) (2018) Classifying acute ischemic stroke onset time using deep imaging features. AMIA Annu Symp Proc"},{"issue":"7","key":"3745_CR5","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1109\/TMI.2019.2901445","volume":"38","author":"KC Ho","year":"2019","unstructured":"Ho KC, Speier W, Zhang H, Scalzo F, El-Saden S, Arnold CW (2019) A machine learning approach for classifying ischemic stroke onset time from imaging. IEEE Trans Med Imaging 38(7):1666\u20131676. https:\/\/doi.org\/10.1109\/TMI.2019.2901445","journal-title":"IEEE Trans Med Imaging"},{"key":"3745_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2024.106897","volume":"100","author":"J Ying","year":"2025","unstructured":"Ying J, Xin Y, Wang C (2025) A prior guide feature enhancement network for classifying ischaemic stroke onset time based on DWI and FLAIR imaging. Biomed Signal Process Control 100:1\u201312. https:\/\/doi.org\/10.1016\/j.bspc.2024.106897","journal-title":"Biomed Signal Process Control"},{"key":"3745_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.nicl.2021.102744","volume":"31","author":"H Zhu","year":"2021","unstructured":"Zhu H, Jiang L, Zhang H, Luo L, Chen Y, Chen Y (2021) An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. NeuroImage Clin 31:1\u20139. https:\/\/doi.org\/10.1016\/j.nicl.2021.102744","journal-title":"NeuroImage Clin"},{"key":"3745_CR8","doi-asserted-by":"publisher","unstructured":"Zhang Y-Q, Liu A-F, Man F-Y et al (2022) MRI radiomic features-based machine learning approach to classify ischemic stroke onset time. J Neurol 1\u201311. https:\/\/doi.org\/10.1007\/s00415-021-10638-y","DOI":"10.1007\/s00415-021-10638-y"},{"key":"3745_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fmed.2023.1085437","volume":"10","author":"H Gao","year":"2023","unstructured":"Gao H, Bian Y, Cheng G et al (2023) Identifying patients with acute ischemic stroke within a 6-h window for the treatment of endovascular thrombectomy using deep learning and perfusion imaging. Front Med 10:1\u20138. https:\/\/doi.org\/10.3389\/fmed.2023.1085437","journal-title":"Front Med"},{"key":"3745_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-021-00678-1","volume":"21","author":"X Wen","year":"2021","unstructured":"Wen X, Shu Z, Li Y, Hu X, Gong X (2021) Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion. BMC Med Imaging 21:1\u201311. https:\/\/doi.org\/10.1186\/s12880-021-00678-1","journal-title":"BMC Med Imaging"},{"key":"3745_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jns.2020.116730","volume":"412","author":"X Yao","year":"2020","unstructured":"Yao X, Mao L, Lv S, Ren Z, Li W, Ren K (2020) CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time. J Neurol Sci 412:1\u20136. https:\/\/doi.org\/10.1016\/j.jns.2020.116730","journal-title":"J Neurol Sci"},{"key":"3745_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2024.103250","volume":"97","author":"J Sun","year":"2024","unstructured":"Sun J, Liu Y, Xi Y et al (2024) Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke. Med Image Anal 97:1\u201315. https:\/\/doi.org\/10.1016\/j.media.2024.103250","journal-title":"Med Image Anal"},{"key":"3745_CR13","doi-asserted-by":"publisher","unstructured":"Liu Z, Zhang S, Wang Y et al (2024) Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging. Neuroradiol 1\u201312. https:\/\/doi.org\/10.1007\/s00234-024-03353-8","DOI":"10.1007\/s00234-024-03353-8"},{"issue":"6","key":"3745_CR14","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1007\/s00330-021-08493-6","volume":"32","author":"L Jiang","year":"2022","unstructured":"Jiang L, Wang S, Ai Z et al (2022) Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI. Eur Radiol 32(6):3661\u20133669. https:\/\/doi.org\/10.1007\/s00330-021-08493-6","journal-title":"Eur Radiol"},{"issue":"3","key":"3745_CR15","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1161\/STROKEAHA.119.027611","volume":"51","author":"H Lee","year":"2020","unstructured":"Lee H, Lee E-J, Ham S et al (2020) Machine learning approach to identify stroke within 4.5 hours. Stroke 51(3):860\u2013866. https:\/\/doi.org\/10.1161\/STROKEAHA.119.027611","journal-title":"Stroke"},{"issue":"1","key":"3745_CR16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1159\/000338389","volume":"1","author":"X Liu","year":"2012","unstructured":"Liu X (2012) Beyond the time window of intravenous thrombolysis: standing by or by stenting? Interventional Neurol 1(1):3\u201315. https:\/\/doi.org\/10.1159\/000338389","journal-title":"Interventional Neurol"},{"issue":"7","key":"3745_CR17","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1177\/17474930211051531","volume":"17","author":"C Jianbo","year":"2022","unstructured":"Jianbo C, Hanqi P, Yihao C et al (2022) Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage. Int J Stroke 17(7):785\u2013792. https:\/\/doi.org\/10.1177\/17474930211051531","journal-title":"Int J Stroke"},{"key":"3745_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/44895","volume":"25","author":"J Feng","year":"2023","unstructured":"Feng J, Zhang Q, Wu F, Peng J, Li Z, Chen Z (2023) The value of applying machine learning in predicting the time of symptom onset in stroke patients: systematic review and Meta-Analysis. J Med Internet Res 25:1\u201315. https:\/\/doi.org\/10.2196\/44895","journal-title":"J Med Internet Res"},{"issue":"n71","key":"3745_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372(n71):1\u20139. https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"3745_CR20","doi-asserted-by":"publisher","unstructured":"McGarry B, Zihni E, Knight M et al (2022) A feasibility study on applying deep learning methods on quantitative T2 MRI relaxation times for stroke onset time estimation. Eur Stroke J 7(1_suppl): 401. https:\/\/doi.org\/10.1177\/23969873221087559","DOI":"10.1177\/23969873221087559"},{"issue":"6","key":"3745_CR21","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.13481\/j.1671-587X.20230631","volume":"49","author":"X Canfei","year":"2023","unstructured":"Canfei X, Yan H, Kaizhi Z (2023) Acute ischemic stroke patients with onset time within 4.5\u00a0h identified by machine learning combined with magnetic resonance diffusion weighted imaging. J Jilin Univ Med Ed 49(6):1635\u20131641. https:\/\/doi.org\/10.13481\/j.1671-587X.20230631","journal-title":"J Jilin Univ Med Ed"},{"key":"3745_CR22","unstructured":"Jiang L, Zhou L, Ai Z et al (2021) Prediction of the onset time of acute stroke by deep learning based on DWI and FLAIR. Chin J Radiol 811\u2013816"},{"issue":"6","key":"3745_CR23","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1111\/jon.13043","volume":"32","author":"JS Polson","year":"2022","unstructured":"Polson JS, Zhang H, Nael K et al (2022) Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning. J Neuroimaging 32(6):1153\u20131160. https:\/\/doi.org\/10.1111\/jon.13043","journal-title":"J Neuroimaging"},{"issue":"5","key":"3745_CR24","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3174\/ajnr.A8170","volume":"45","author":"E Tavakkol","year":"2024","unstructured":"Tavakkol E, Kihira S, McArthur M et al (2024) Automated assessment of the DWI-FLAIR mismatch in patients with acute ischemic stroke: added value to routine clinical practice. AJNR Am J Neuroradiol 45(5):562\u2013567. https:\/\/doi.org\/10.3174\/ajnr.A8170","journal-title":"AJNR Am J Neuroradiol"},{"issue":"10","key":"3745_CR25","doi-asserted-by":"publisher","first-page":"6808","DOI":"10.1007\/s00330-024-10619-5","volume":"34","author":"L Jiang","year":"2024","unstructured":"Jiang L, Sun J, Wang Y et al (2024) Diffusion-\/perfusion-weighted imaging fusion to automatically identify stroke within 4.5\u00a0h. Eur Radiol 34(10):6808\u20136819. https:\/\/doi.org\/10.1007\/s00330-024-10619-5","journal-title":"Eur Radiol"},{"key":"3745_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.jmir.2024.101588","volume":"55","author":"L Liu","year":"2024","unstructured":"Liu L, Mao X, Yang S et al (2024) Imaging features to predict the onset time of acute stroke: model constructionand efficacy evaluationresearch. J Med Imaging Radiat Sci 55:39\u201340","journal-title":"J Med Imaging Radiat Sci"},{"issue":"1","key":"3745_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-10804-6","volume":"15","author":"E Namgung","year":"2025","unstructured":"Namgung E, Kim YS, Lee EJ et al (2025) Deep learning to identify stroke within 4.5\u00a0h using DWI and FLAIR in a prospective multicenter study. Sci Rep 15(1):1\u201311. https:\/\/doi.org\/10.1038\/s41598-025-10804-6","journal-title":"Sci Rep"},{"key":"3745_CR28","doi-asserted-by":"publisher","unstructured":"Zhao Y, Wang XY, Ding Y et al (2024) Ischemic Stroke Onset Time Classification Using Automated Deep Learning Assessment Based on Diffusion-Weighted Imaging. Available from: SSRN: https:\/\/ssrn.com\/abstract=5093172 or https:\/\/doi.org\/10.2139\/ssrn.5093172","DOI":"10.2139\/ssrn.5093172"},{"issue":"11","key":"3745_CR29","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/S1474-4422(11)70192","volume":"10","author":"G Thomalla","year":"2011","unstructured":"Thomalla G, Cheng B, Ebinger M et al (2011) DWI-FLAIR mismatch for the identification of patients with acute ischaemic stroke within 4\u00b7 5\u00a0h of symptom onset (PRE-FLAIR): a multicentre observational study. Lancet Neurol 10(11):978\u2013986. https:\/\/doi.org\/10.1016\/S1474-4422(11)70192-","journal-title":"Lancet Neurol"},{"key":"3745_CR30","doi-asserted-by":"publisher","unstructured":"Potreck A, Weyland C, Seker F et al (2022) Accuracy and prognostic role of NCCT-ASPECTS depend on time from acute stroke symptom-onset for both human and machine-learning based evaluation. Clin Neuroradiol 1\u20138. https:\/\/doi.org\/10.1007\/s00062-021-01110-5","DOI":"10.1007\/s00062-021-01110-5"}],"container-title":["Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-025-03745-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00234-025-03745-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-025-03745-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:26:49Z","timestamp":1761118009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00234-025-03745-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":30,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["3745"],"URL":"https:\/\/doi.org\/10.1007\/s00234-025-03745-4","relation":{},"ISSN":["0028-3940","1432-1920"],"issn-type":[{"type":"print","value":"0028-3940"},{"type":"electronic","value":"1432-1920"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"18 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This article is a review study and does not contain any data or other information from studies or experiments conducted on human subjects. No human subjects participated in this study; therefore, informed consent is not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent of participation"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}