{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:56:37Z","timestamp":1786982197386,"version":"build-2736575974"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R1NS085419"],"award-info":[{"award-number":["R1NS085419"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["K23NS099440"],"award-info":[{"award-number":["K23NS099440"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["K23NS099487"],"award-info":[{"award-number":["K23NS099487"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["P30NS098577"],"award-info":[{"award-number":["P30NS098577"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neurocrit Care"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s12028-021-01325-x","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T13:04:03Z","timestamp":1629464643000},"page":"471-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Accelerating Prediction of Malignant Cerebral Edema After Ischemic Stroke with Automated Image Analysis and Explainable Neural Networks"],"prefix":"10.1007","volume":"36","author":[{"given":"Hossein Mohammadian","family":"Foroushani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Hamzehloo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Heitsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Slowik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Strbian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Moo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel S.","family":"Marcus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5167-5097","authenticated-orcid":false,"given":"Rajat","family":"Dhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"1325_CR1","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1001\/archneur.1996.00550040037012","volume":"53","author":"W Hacke","year":"1996","unstructured":"Hacke W, Schwab S, Horn M, et al. \u201cMalignant\u201d middle cerebral artery territory infarction: clinical course and prognostic signs. Arch Neurol. 1996;53:309\u201315.","journal-title":"Arch Neurol"},{"key":"1325_CR2","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/S1474-4422(07)70036-4","volume":"6","author":"K Vahedi","year":"2007","unstructured":"Vahedi K, Hofmeijer J, Juettler E, et al. Early decompressive surgery in malignant infarction of the middle cerebral artery: a pooled analysis of three randomised controlled trials. Lancet Neurol. 2007;6:215\u201322.","journal-title":"Lancet Neurol"},{"issue":"9","key":"1325_CR3","doi-asserted-by":"publisher","first-page":"2619","DOI":"10.1161\/STROKEAHA.118.024766","volume":"50","author":"DS Liebeskind","year":"2019","unstructured":"Liebeskind DS, Juttler E, Shapovalov Y, et al. Cerebral Edema associated with large hemispheric infarction. Stroke. 2019;50(9):2619\u201325.","journal-title":"Stroke"},{"issue":"12","key":"1325_CR4","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1161\/STROKEAHA.118.022001","volume":"49","author":"S Wu","year":"2018","unstructured":"Wu S, Yuan R, Wang Y, et al. Early prediction of malignant brain edema after ischemic stroke. Stroke. 2018;49(12):2918\u201327.","journal-title":"Stroke"},{"issue":"1","key":"1325_CR5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1159\/000363619","volume":"38","author":"C MacCallum","year":"2014","unstructured":"MacCallum C, Churilov L, Mitchell P, Dowling R, Yan B. Low Alberta Stroke Program Early CT score (ASPECTS) associated with malignant middle cerebral artery infarction. Cerebrovasc Dis. 2014;38(1):39\u201345.","journal-title":"Cerebrovasc Dis"},{"key":"1325_CR6","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1002\/ana.22125","volume":"68","author":"G Thomalla","year":"2010","unstructured":"Thomalla G, Hartmann F, Juettler E, et al. Prediction of malignant middle cerebral artery infarction by magnetic resonance imaging within 6 hours of symptom onset: a prospective multicenter observational study. Ann Neurol. 2010;68:435\u201345.","journal-title":"Ann Neurol"},{"issue":"3","key":"1325_CR7","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1161\/01.STR.0000204054.30458.e8","volume":"37","author":"B Bosche","year":"2006","unstructured":"Bosche B, Hamann GF, Dohmen C, Graf R. There is more to it than: the greater the infarction volume, the more probable is a malignant MCA infarction. Stroke. 2006;37(3):762\u20133.","journal-title":"Stroke"},{"key":"1325_CR8","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1161\/01.STR.0000083624.74929.32","volume":"34","author":"C Dohmen","year":"2003","unstructured":"Dohmen C, Bosche B, Graf R, et al. Prediction of malignant course in MCA infarction by PET and microdialysis. Stroke J Cerebral Circ. 2003;34:2152\u20138.","journal-title":"Stroke J Cerebral Circ"},{"issue":"5","key":"1325_CR9","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s10072-012-1102-1","volume":"34","author":"L Burghaus","year":"2013","unstructured":"Burghaus L, Liu WC, Dohmen C, et al. Prognostic value of electroencephalography and evoked potentials in the early course of malignant middle cerebral artery infarction. Neurol Sci. 2013;34(5):671\u20138.","journal-title":"Neurol Sci"},{"key":"1325_CR10","doi-asserted-by":"publisher","first-page":"3403","DOI":"10.1161\/STROKEAHA.111.619734","volume":"42","author":"J Minnerup","year":"2011","unstructured":"Minnerup J, Wersching H, Ringelstein EB, et al. Prediction of malignant middle cerebral artery infarction using computed tomography-based intracranial volume reserve measurements. Stroke. 2011;42:3403\u20139.","journal-title":"Stroke"},{"key":"1325_CR11","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.119.026853","author":"F Kauw","year":"2019","unstructured":"Kauw F, Bennink E, de Jong H, et al. Intracranial cerebrospinal fluid volume as a predictor of malignant middle cerebral artery infarction. Stroke. 2019. https:\/\/doi.org\/10.1161\/STROKEAHA.119.026853.","journal-title":"Stroke"},{"issue":"7","key":"1325_CR12","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1161\/STROKEAHA.117.016733","volume":"48","author":"CJ Ong","year":"2017","unstructured":"Ong CJ, Gluckstein J, Laurido-Soto O, et al. Enhanced detection of edema in malignant anterior circulation stroke (EDEMA) score: a risk prediction tool. Stroke. 2017;48(7):1969\u201372.","journal-title":"Stroke"},{"issue":"3","key":"1325_CR13","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s12028-015-0204-z","volume":"24","author":"R Dhar","year":"2016","unstructured":"Dhar R, Yuan K, Kulik T, et al. CSF volumetric analysis for quantification of cerebral edema after hemispheric infarction. Neurocrit Care. 2016;24(3):420\u20137.","journal-title":"Neurocrit Care"},{"key":"1325_CR14","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.nicl.2016.09.018","volume":"12","author":"Y Chen","year":"2016","unstructured":"Chen Y, Dhar R, Heitsch L, et al. Automated quantification of cerebral edema following hemispheric infarction: application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs. Neuroimage Clin. 2016;12:673\u201380.","journal-title":"Neuroimage Clin"},{"issue":"2","key":"1325_CR15","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1161\/STROKEAHA.119.027895","volume":"51","author":"R Dhar","year":"2020","unstructured":"Dhar R, Chen Y, Hamzehloo A, et al. Reduction in cerebrospinal fluid volume as an early quantitative biomarker of cerebral edema after ischemic stroke. Stroke. 2020;51(2):462\u20137.","journal-title":"Stroke"},{"key":"1325_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-020-01056-5","author":"HM Foroushani","year":"2020","unstructured":"Foroushani HM, Hamzehloo A, Kumar A, et al. Quantitative serial CT imaging-derived features improve prediction of malignant cerebral edema after ischemic stroke. Neurocrit Care. 2020. https:\/\/doi.org\/10.1007\/s12028-020-01056-5.","journal-title":"Neurocrit Care"},{"key":"1325_CR17","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X211018210","author":"R Dhar","year":"2021","unstructured":"Dhar R, Hamzehloo A, Kumar A, et al. Hemispheric CSF volume ratio quantifies progression and severity of cerebral edema after acute hemispheric stroke. J Cereb Blood Flow Metab. 2021. https:\/\/doi.org\/10.1177\/0271678X211018210.","journal-title":"J Cereb Blood Flow Metab"},{"key":"1325_CR18","doi-asserted-by":"publisher","first-page":"687","DOI":"10.3389\/fneur.2018.00687","volume":"9","author":"R Dhar","year":"2018","unstructured":"Dhar R, Chen Y, An H, Lee JM. Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients. Front Neurol. 2018;9:687.","journal-title":"Front Neurol"},{"issue":"4","key":"1325_CR19","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.neuroimage.2012.03.020","volume":"61","author":"C Rorden","year":"2012","unstructured":"Rorden C, Bonilha L, Fridriksson J, Bender B, Karnath HO. Age-specific CT and MRI templates for spatial normalization. Neuroimage. 2012;61(4):957\u201365.","journal-title":"Neuroimage"},{"key":"1325_CR20","doi-asserted-by":"publisher","first-page":"116607","DOI":"10.1016\/j.jns.2019.116607","volume":"409","author":"J Miao","year":"2020","unstructured":"Miao J, Song X, Sun W, et al. Predictors of malignant cerebral edema in cerebral artery infarction: a meta-analysis. J Neurol Sci. 2020;409:116607.","journal-title":"J Neurol Sci"},{"issue":"8","key":"1325_CR21","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt G, Mosquera C, Napoles G. A review on the long short-term memory model. Artif Intell Rev. 2020;53(8):5929\u201355.","journal-title":"Artif Intell Rev"},{"key":"1325_CR22","first-page":"460","volume":"2018","author":"W Ge","year":"2018","unstructured":"Ge W, Huh JW, Park YR, et al. An interpretable ICU mortality prediction model based on logistic regression and recurrent neural networks with LSTM units. AMIA Ann Symp Proc. 2018;2018:460\u20139.","journal-title":"AMIA Ann Symp Proc"},{"issue":"1","key":"1325_CR23","doi-asserted-by":"publisher","first-page":"8827","DOI":"10.1038\/s41598-021-88226-3","volume":"11","author":"D Shung","year":"2021","unstructured":"Shung D, Huang J, Castro E, et al. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Sci Rep. 2021;11(1):8827.","journal-title":"Sci Rep"},{"issue":"1","key":"1325_CR24","doi-asserted-by":"publisher","first-page":"ooab004","DOI":"10.1093\/jamiaopen\/ooab004","volume":"4","author":"Y Sun","year":"2021","unstructured":"Sun Y, Kaur R, Gupta S, et al. Development and validation of high definition phenotype-based mortality prediction in critical care units. JAMIA Open. 2021;4(1):ooab004.","journal-title":"JAMIA Open"},{"key":"1325_CR25","first-page":"1003","volume":"2020","author":"M Pediaditis","year":"2020","unstructured":"Pediaditis M, Ciubotaru AN, Brunschwiler T, et al. Machine learning techniques for personalized detection of epileptic events in clinical video recordings. AMIA Ann Symp Proc. 2020;2020:1003\u201311.","journal-title":"AMIA Ann Symp Proc"},{"issue":"1","key":"1325_CR26","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1097\/SHK.0000000000001692","volume":"56","author":"GH Kwak","year":"2021","unstructured":"Kwak GH, Ling L, Hui P. Predicting the need for vasopressors in the intensive care unit using an attention based deep learning model. Shock. 2021;56(1):73\u201379.","journal-title":"Shock"},{"key":"1325_CR27","doi-asserted-by":"publisher","first-page":"103986","DOI":"10.1016\/j.ijmedinf.2019.103986","volume":"132","author":"Y Ge","year":"2019","unstructured":"Ge Y, Wang Q, Wang L, et al. Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inf. 2019;132:103986.","journal-title":"Int J Med Inf"},{"issue":"3","key":"1325_CR28","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015;10(3):e0118432.","journal-title":"PLoS One"},{"issue":"1","key":"1325_CR29","doi-asserted-by":"publisher","first-page":"3852","DOI":"10.1038\/s41467-020-17431-x","volume":"11","author":"SM Lauritsen","year":"2020","unstructured":"Lauritsen SM, Kristensen M, Olsen MV, et al. Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat Commun. 2020;11(1):3852.","journal-title":"Nat Commun"},{"issue":"1","key":"1325_CR30","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s12028-019-00844-y","volume":"32","author":"Y Cheng","year":"2020","unstructured":"Cheng Y, Wu S, Wang Y, et al. External validation and modification of the EDEMA score for predicting malignant brain EDEMA after acute ischemic stroke. Neurocrit Care. 2020;32(1):104\u201312.","journal-title":"Neurocrit Care"},{"issue":"12","key":"1325_CR31","doi-asserted-by":"publisher","first-page":"3424","DOI":"10.1161\/STROKEAHA.119.027062","volume":"50","author":"G Broocks","year":"2019","unstructured":"Broocks G, Kemmling A, Meyer L, et al. Computed tomography angiography collateral profile is directly linked to early EDEMA progression rate in acute ischemic stroke. Stroke. 2019;50(12):3424\u201330.","journal-title":"Stroke"},{"issue":"5","key":"1325_CR32","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1161\/01.STR.17.5.865","volume":"17","author":"L Berger","year":"1986","unstructured":"Berger L, Hakim AM. The association of hyperglycemia with cerebral edema in stroke. Stroke. 1986;17(5):865\u201371.","journal-title":"Stroke"},{"key":"1325_CR33","doi-asserted-by":"publisher","first-page":"135046","DOI":"10.1016\/j.neulet.2020.135046","volume":"730","author":"E Kirsch","year":"2020","unstructured":"Kirsch E, Szejko N, Falcone GJ. Genetic underpinnings of cerebral edema in acute brain injury: an opportunity for pathway discovery. Neurosci Lett. 2020;730:135046.","journal-title":"Neurosci Lett"},{"key":"1325_CR34","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1161\/01.str.0000441965.15164.d6","volume":"45","author":"EFM Wijdicks","year":"2014","unstructured":"Wijdicks EFM, Sheth KN, Carter BS, et al. Recommendations for the management of cerebral and cerebellar infarction with swelling: a statement for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke J Cerebral Circ. 2014;45:1222\u201338.","journal-title":"Stroke J Cerebral Circ"},{"key":"1325_CR35","doi-asserted-by":"publisher","first-page":"134879","DOI":"10.1016\/j.neulet.2020.134879","volume":"724","author":"R Dhar","year":"2020","unstructured":"Dhar R. Automated quantitative assessment of cerebral edema after ischemic stroke using CSF volumetrics. Neurosci Lett. 2020;724:134879.","journal-title":"Neurosci Lett"},{"key":"1325_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30018-2","author":"H-C Thorsen-Mayer","year":"2020","unstructured":"Thorsen-Mayer H-C, Nielson AB, Nielsen AP, et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digital Health. 2020. https:\/\/doi.org\/10.1016\/S2589-7500(20)30018-2.","journal-title":"Lancet Digital Health"},{"issue":"9","key":"1325_CR37","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1161\/STROKEAHA.117.018223","volume":"48","author":"M Thoren","year":"2017","unstructured":"Thoren M, Azevedo E, Dawson J, et al. Predictors for cerebral edema in acute ischemic stroke treated with intravenous thrombolysis. Stroke. 2017;48(9):2464\u201371.","journal-title":"Stroke"},{"issue":"3","key":"1325_CR38","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1056\/NEJMc2104626","volume":"385","author":"SG Finlayson","year":"2021","unstructured":"Finlayson SG, Subbaswamy A, Singh K, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283\u20136.","journal-title":"N Engl J Med"},{"issue":"10","key":"1325_CR39","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg SM, Nair B, Vavilala MS, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749\u201360.","journal-title":"Nat Biomed Eng"},{"key":"1325_CR40","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.nicl.2014.02.003","volume":"4","author":"P Bentley","year":"2014","unstructured":"Bentley P, Ganesalingam J, Carlton Jones AL, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin. 2014;4:635\u201340.","journal-title":"Neuroimage Clin"}],"container-title":["Neurocritical Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12028-021-01325-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12028-021-01325-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12028-021-01325-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T02:10:36Z","timestamp":1648519836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12028-021-01325-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,20]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["1325"],"URL":"https:\/\/doi.org\/10.1007\/s12028-021-01325-x","relation":{},"ISSN":["1541-6933","1556-0961"],"issn-type":[{"value":"1541-6933","type":"print"},{"value":"1556-0961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,20]]},"assertion":[{"value":"30 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"JML reports grants from Biogen, personal fees from Regenera, outside the submitted work. All other authors have nothing to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"All research was conducted under approval of the institutional ethics review board and all subjects provided informed consent for data collection.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval\/Informed Consent"}}]}}