{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:24:39Z","timestamp":1774542279643,"version":"3.50.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81870218"],"award-info":[{"award-number":["81870218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p><jats:bold>Objective:<\/jats:bold> To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD).<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS. Pre-operative AIS risk prediction models based on different machine learning algorithm was established with clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input. The performance of the difference models was evaluated using the receiver operating characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA).<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%). The cohort was split into a training (211, 70% patients) and validation cohort (89, 30% patients) according to stratified sampling strategy. The constructed deep neural network model had the best performance on the discrimination of AIS group compare with other machine learning model, with an accuracy of 0.934 (95% CI: 0.891\u20130.963), 0.921 (95% CI: 0.845\u20130.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of 0.982 (95% CI: 0.967\u20130.997), 0.964 (95% CI: 0.932\u20130.997) in the training and validation cohort, respectively.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> The established risk prediction model based on the deep neural network method may have the big potential to evaluate the risk of pre-operative AIS in patients with ATAAD.<\/jats:p>","DOI":"10.3389\/fneur.2021.792678","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T05:12:32Z","timestamp":1640236352000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients"],"prefix":"10.3389","volume":"12","author":[{"given":"Hongliang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanqiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijia","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialiang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixun","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minwen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"S175","DOI":"10.1161\/CIRCULATIONAHA.112.000327","article-title":"Stroke and outcomes in patients with acute type A aortic dissection","volume":"128","author":"Bossone","year":"2013","journal-title":"Circulation."},{"key":"B2","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1080\/20009666.2017.1379854","article-title":"An extensive chronic aortic dissection presenting with acute embolic stroke","volume":"7","author":"Basnet","year":"2017","journal-title":"J Community Hosp Intern Med Perspect."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1007\/s00595-016-1381-x","article-title":"Cerebral malperfusion in acute aortic dissection","volume":"46","author":"Orihashi","year":"2016","journal-title":"Surg Today."},{"key":"B4","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s11748-018-0956-4","article-title":"Acute ischemic stroke as a complication of Stanford type A acute aortic dissection: a review and proposed clinical recommendations for urgent diagnosis","volume":"66","author":"Koga","year":"2018","journal-title":"Gen Thorac Cardiovasc Surg."},{"key":"B5","doi-asserted-by":"publisher","first-page":"543","DOI":"10.2169\/internalmedicine.49.3026","article-title":"Hyper-acute stroke patients associated with aortic dissection","volume":"49","author":"Iguchi","year":"2010","journal-title":"Intern Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1016\/j.jvs.2013.03.042","article-title":"Management of common carotid artery dissection due to extension from acute type A (DeBakey I) aortic dissection","volume":"58","author":"Charlton-Ouw","year":"2013","journal-title":"J Vasc Surg."},{"key":"B7","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1016\/j.jacc.2015.04.030","article-title":"The impact of pre-operative malperfusion on outcome in acute type A aortic dissection: results from the GERAADA registry","volume":"65","author":"Czerny","year":"2015","journal-title":"J Am Coll Cardiol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.neunet.2019.10.014","article-title":"A review on neural network models of schizophrenia and autism spectrum disorder","volume":"122","author":"Lanillos","year":"2020","journal-title":"Neural Netw"},{"key":"B9","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.conb.2019.02.003","article-title":"Deep neural network models of sensory systems: windows onto the role of task constraints","volume":"55","author":"Kell","year":"2019","journal-title":"Curr Opin Neurobiol"},{"key":"B10","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.324","article-title":"Focal Loss for Dense Object Detection","volume-title":"2017 IEEE International Conference on Computer Vision (ICCV)","author":"Lin","year":""},{"key":"B11","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1093\/ejcts\/ezz351","article-title":"Computed tomography angiography findings predict the risk factors for pre-operative acute ischaemic stroke in patients with acute type A aortic dissection","volume":"57","author":"Zhao","year":"2020","journal-title":"Eur J Cardiothorac Surg."},{"key":"B12","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.chemolab.2011.12.002","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res."},{"key":"B13","doi-asserted-by":"publisher","first-page":"448","DOI":"10.3389\/fnins.2019.00448","article-title":"Abnormal resting-state functional connectivity in the whole brain in lifelong premature ejaculation patients based on machine learning approach","volume":"13","author":"Xu","year":"2019","journal-title":"Front Neurosci."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine","volume":"24","author":"Noble","year":"2006","journal-title":"Nature Biotechnol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int J Remote Sens."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput"},{"key":"B17","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"B18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.athoracsur.2020.01.026","article-title":"Immediate surgery in acute type A dissection and neurologic dysfunction: fighting the inevitable","volume":"110","author":"Dumfarth","year":"2020","journal-title":"Ann Thorac Surg."},{"key":"B19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.21037\/jovs.2018.03.13","article-title":"Malperfusion syndromes in type A aortic dissection: what we have learned from IRAD","volume":"4","author":"Berretta","year":"2018","journal-title":"J Vis Surg."},{"key":"B20","doi-asserted-by":"publisher","first-page":"77","DOI":"10.21037\/jovs.2018.03.20","article-title":"Cerebral perfusion issues in type A aortic dissection","volume":"4","author":"Pacini","year":"2018","journal-title":"J Vis Surg."}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.792678\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T05:12:37Z","timestamp":1640236357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.792678\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,23]]},"references-count":20,"alternative-id":["10.3389\/fneur.2021.792678"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2021.792678","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,23]]},"article-number":"792678"}}