{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:50:38Z","timestamp":1776826238331,"version":"3.51.2"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Neuroradiol"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00062-024-01471-7","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T14:06:09Z","timestamp":1732543569000},"page":"185-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography"],"prefix":"10.1007","volume":"35","author":[{"given":"Jan","family":"Boriesosdick","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iram","family":"Shahzadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogdan","family":"Georgescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eli","family":"Gibson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lynn Johann","family":"Frohwein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saher","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nina P.","family":"Haag","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Horstmeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Moenninghoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julius Henning","family":"Niehoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Surov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Borggrefe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan Robert","family":"Kroeger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"issue":"5","key":"1471_CR1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S1474-4422(19)30034-1","volume":"18","author":"C Johnson Owens","year":"2019","unstructured":"Johnson Owens\u00a0C, et\u00a0al. Global, regional, and national burden of stroke, 1990\u20132016: a\u00a0systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):439\u201358.","journal-title":"Lancet Neurol"},{"issue":"10262","key":"1471_CR2","first-page":"1562","volume":"396","author":"T Vos","year":"2020","unstructured":"Vos\u00a0T. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: a\u00a0systematic analysis for the Global Burden of Disease Study 2019 (vol 396, pg 1204, 2020). Lancet. 2020;396(10262):1562\u20131562.","journal-title":"Lancet"},{"issue":"5","key":"1471_CR3","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.clinimag.2005.01.030","volume":"29","author":"T Kinoshita","year":"2005","unstructured":"Kinoshita\u00a0T, et\u00a0al. CT angiography in the evaluation of intracranial occlusive disease with collateral circulation: comparison with MR angiography. Clin Imaging. 2005;29(5):303\u20136.","journal-title":"Clin Imaging"},{"issue":"12","key":"1471_CR4","doi-asserted-by":"publisher","first-page":"e344","DOI":"10.1161\/STR.0000000000000211","volume":"50","author":"WJ Powers","year":"2019","unstructured":"Powers\u00a0WJ, et\u00a0al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a\u00a0guideline for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke. 2019;50(12):e344\u2013e418.","journal-title":"Stroke"},{"issue":"4","key":"1471_CR5","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1002\/ana.26435","volume":"92","author":"IR Chavva","year":"2022","unstructured":"Chavva\u00a0IR, et\u00a0al. Deep learning applications for acute stroke management. Ann Neurol. 2022;92(4):574\u201387.","journal-title":"Ann Neurol"},{"key":"1471_CR6","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.wneu.2021.12.004","volume":"159","author":"NA Shlobin","year":"2022","unstructured":"Shlobin\u00a0NA, et\u00a0al. Artificial intelligence for large-vessel occlusion stroke: a\u00a0systematic review. World Neurosurg. 2022;159:207\u201320.","journal-title":"World Neurosurg"},{"issue":"2","key":"1471_CR7","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1159\/000519125","volume":"51","author":"G Rodrigues","year":"2022","unstructured":"Rodrigues\u00a0G, et\u00a0al. Automated large artery occlusion detection in stroke: a\u00a0single-center validation study of an artificial intelligence algorithm. Cerebrovasc Dis. 2022;51(2):259\u201364.","journal-title":"Cerebrovasc Dis"},{"issue":"2","key":"1471_CR8","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3174\/ajnr.A6923","volume":"42","author":"A Yahav-Dovrat","year":"2021","unstructured":"Yahav-Dovrat\u00a0A, et\u00a0al. Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a\u00a0comprehensive stroke center. AJNR Am J Neuroradiol. 2021;42(2):247\u201354.","journal-title":"AJNR Am J Neuroradiol"},{"key":"1471_CR9","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.3389\/fneur.2022.1026609","volume":"13","author":"J Schlossman","year":"2022","unstructured":"Schlossman\u00a0J, et\u00a0al. Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a\u00a0comprehensive stroke center. Front Neurol. 2022;13:2315.","journal-title":"Front Neurol"},{"issue":"8","key":"1471_CR10","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1136\/neurintsurg-2021-017842","volume":"14","author":"SPR Luijten","year":"2022","unstructured":"Luijten\u00a0SPR, et\u00a0al. Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography. J\u00a0NeuroIntervent Surg. 2022;14(8):794\u20138.","journal-title":"J NeuroIntervent Surg"},{"issue":"1","key":"1471_CR11","doi-asserted-by":"publisher","first-page":"4938","DOI":"10.1038\/s41467-023-40564-8","volume":"14","author":"G Brugnara","year":"2023","unstructured":"Brugnara\u00a0G, et\u00a0al. Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nat Commun. 2023;14(1):4938.","journal-title":"Nat Commun"},{"issue":"1","key":"1471_CR12","doi-asserted-by":"publisher","first-page":"8834","DOI":"10.1038\/s41598-023-33723-w","volume":"13","author":"O Bagcilar","year":"2023","unstructured":"Bagcilar\u00a0O, Alis\u00a0D, Alis\u00a0C, Seker\u00a0ME, Yergin\u00a0M, Ustundag\u00a0A, Karaarslan\u00a0E. Automated LVO detection and collateral scoring on CTA using a\u00a03D self-configuring object detection network: a\u00a0multi-center study. Sci Rep. 2023;13(1):8834.","journal-title":"Sci Rep"},{"issue":"18","key":"1471_CR13","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.3390\/diagnostics13182863","volume":"13","author":"R Schwarz","year":"2023","unstructured":"Schwarz\u00a0R, et\u00a0al. Automated Intracranial clot detection: a\u00a0promising tool for vascular occlusion detection in non-enhanced CT. Diagnostics. 2023;13(18):2863.","journal-title":"Diagnostics"},{"key":"1471_CR14","doi-asserted-by":"publisher","first-page":"100911","DOI":"10.1016\/j.imu.2022.100911","volume":"29","author":"AR Luca","year":"2022","unstructured":"Luca\u00a0AR, et\u00a0al. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. Informatics Med Unlocked. 2022;29:100911.","journal-title":"Informatics Med Unlocked"},{"issue":"7","key":"1471_CR15","first-page":"441","volume":"58","author":"T Flohr","year":"2023","unstructured":"Flohr\u00a0T, Schmidt\u00a0B. Technical basics and clinical benefits of photon-counting CT. Invest Radiol. 2023;58(7):441\u201350.","journal-title":"Invest Radiol"},{"key":"1471_CR16","series-title":"Proceedings, Part III 20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_58","volume-title":"Automatic liver segmentation using an adversarial image-to-image network","author":"D Yang","year":"2017","unstructured":"Yang\u00a0D, et\u00a0al. Automatic liver segmentation using an adversarial image-to-image network. Proceedings, Part III 20. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017: 20th\u00a0International Conference; September\u00a011\u201313; Quebec City. Springer; 2017."},{"issue":"1","key":"1471_CR17","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/TPAMI.2017.2782687","volume":"41","author":"F-C Ghesu","year":"2017","unstructured":"Ghesu\u00a0F\u2011C, et\u00a0al. Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE Trans Pattern Anal Mach Intell. 2017;41(1):176\u201389.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1471_CR18","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1161\/STROKEAHA.113.003007","volume":"45","author":"D Rodriguez-Luna","year":"2014","unstructured":"Rodriguez-Luna\u00a0D, et\u00a0al. Venous phase of computed tomography angiography increases spot sign detection, but intracerebral hemorrhage expansion is greater in spot signs detected in arterial phase. Stroke. 2014;45(3):734\u20139.","journal-title":"Stroke"},{"issue":"5","key":"1471_CR19","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1177\/19714009211012353","volume":"34","author":"J Adhya","year":"2021","unstructured":"Adhya\u00a0J, et\u00a0al. Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: one year experience. Neuroradiol\u00a0J. 2021;34(5):476\u201381.","journal-title":"Neuroradiol J"},{"key":"1471_CR20","doi-asserted-by":"crossref","unstructured":"Delora A, et\u00a0al. Viz LVO versus Rapid LVO in detection of large vessel occlusion on CT angiography for acute stroke. J\u00a0NeuroIntervent Surg. 2023;.","DOI":"10.1136\/jnis-2023-020445"},{"key":"1471_CR21","doi-asserted-by":"publisher","first-page":"656112","DOI":"10.3389\/fneur.2021.656112","volume":"12","author":"J McLouth","year":"2021","unstructured":"McLouth\u00a0J, et\u00a0al. Validation of a\u00a0deep learning tool in the detection of intracranial hemorrhage and large vessel occlusion. Front Neurol. 2021;12:656112.","journal-title":"Front Neurol"},{"issue":"5","key":"1471_CR22","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1177\/1971400921998952","volume":"34","author":"RA Rava","year":"2021","unstructured":"Rava\u00a0RA, et\u00a0al. Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients. Neuroradiol\u00a0J. 2021;34(5):408\u201317.","journal-title":"Neuroradiol J"},{"issue":"6","key":"1471_CR23","doi-asserted-by":"publisher","first-page":"641","DOI":"10.3174\/ajnr.A7878","volume":"44","author":"K Zhu","year":"2023","unstructured":"Zhu\u00a0K, et\u00a0al. Automated segmentation of intracranial thrombus on NCCT and CTA in patients with acute Ischemic stroke using a\u00a0coarse-to-fine deep learning model. AJNR Am J Neuroradiol. 2023;44(6):641\u20138.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"3","key":"1471_CR24","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1148\/radiol.2020200334","volume":"297","author":"MT Stib","year":"2020","unstructured":"Stib\u00a0MT, et\u00a0al. Detecting large vessel occlusion at multiphase CT angiography by using a\u00a0deep convolutional neural network. Radiology. 2020;297(3):640\u20139.","journal-title":"Radiology"},{"issue":"11","key":"1471_CR25","doi-asserted-by":"publisher","first-page":"106753","DOI":"10.1016\/j.jstrokecerebrovasdis.2022.106753","volume":"31","author":"S Meng","year":"2022","unstructured":"Meng\u00a0S, et\u00a0al. End-to-end artificial intelligence platform for the management of large vessel occlusions: a\u00a0preliminary study. J\u00a0Stroke Cerebrovasc Dis. 2022;31(11):106753.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"10","key":"1471_CR26","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1161\/STROKEAHA.120.030326","volume":"51","author":"M Olive-Gadea","year":"2020","unstructured":"Olive-Gadea\u00a0M, et\u00a0al. Deep learning based software to identify large vessel occlusion on noncontrast computed tomography. Stroke. 2020;51(10):3133\u20137.","journal-title":"Stroke"},{"issue":"11","key":"1471_CR27","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1001\/jamaneurol.2016.2773","volume":"73","author":"A Sarraj","year":"2016","unstructured":"Sarraj\u00a0A, et\u00a0al. Endovascular therapy for acute ischemic stroke with occlusion of the middle cerebral artery M2 segment. JAMA Neurol. 2016;73(11):1291\u20136.","journal-title":"JAMA Neurol"}],"container-title":["Clinical Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00062-024-01471-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00062-024-01471-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00062-024-01471-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T20:30:18Z","timestamp":1739824218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00062-024-01471-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["1471"],"URL":"https:\/\/doi.org\/10.1007\/s00062-024-01471-7","relation":{},"ISSN":["1869-1439","1869-1447"],"issn-type":[{"value":"1869-1439","type":"print"},{"value":"1869-1447","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,25]]},"assertion":[{"value":"26 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"J.\u00a0Boriesosdick, S.\u00a0Saeed, N.P.\u00a0Haag, S.\u00a0Horstmeier, C.\u00a0Moenninghoff, J.H.\u00a0Niehoff and A.\u00a0Surov declare that they have no competing interests. I.\u00a0Shahzadi, L.\u00a0Xie, B.\u00a0Georgescu, E.\u00a0Gibson, L.J.\u00a0Frohwein are employees of Siemens Healthineers. J.\u00a0Borggrefe received honoraria for scientific lectures from Philips Healthcare and Siemens Healthineers. and J.R.\u00a0Kroeger received honoraria for scientific lectures from GE Healthcare and for clinical advisory board membership from Siemens Healthineers.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}