{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:54:04Z","timestamp":1771746844972,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-024-01436-9","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T12:02:29Z","timestamp":1727784149000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms"],"prefix":"10.1186","volume":"24","author":[{"given":"Yi","family":"Shen","sequence":"first","affiliation":[]},{"given":"Chao","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Bingqian","family":"Chu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Song","sequence":"additional","affiliation":[]},{"given":"Yayuan","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9481-9425","authenticated-orcid":false,"given":"Xingwang","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"1436_CR1","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.jocn.2017.10.064","volume":"48","author":"C Grochowski","year":"2018","unstructured":"Grochowski C, Litak J, Kulesza B, Szmygin P, Ziemianek D, Kamieniak P, et al. Size and location correlations with higher rupture risk of intracranial aneurysms. J Clin Neurosci. 2018;48:181\u20134.","journal-title":"J Clin Neurosci"},{"issue":"7","key":"1436_CR2","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/S1474-4422(11)70109-0","volume":"10","author":"MH Vlak","year":"2011","unstructured":"Vlak MH, Algra A, Brandenburg R, Rinkel GJ. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol. 2011;10(7):626\u201336.","journal-title":"Lancet Neurol"},{"issue":"26","key":"1436_CR3","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1056\/NEJMoa1113260","volume":"366","author":"A Morita","year":"2012","unstructured":"Morita A, Kirino T, Hashi K, Aoki N, Fukuhara S, Hashimoto N, et al. The natural course of unruptured cerebral aneurysms in a Japanese cohort. N Engl J Med. 2012;366(26):2474\u201382.","journal-title":"N Engl J Med"},{"issue":"9558","key":"1436_CR4","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/S0140-6736(07)60153-6","volume":"369","author":"J van Gijn","year":"2007","unstructured":"van Gijn J, Kerr RS, Rinkel GJ. Subarachnoid haemorrhage. Lancet. 2007;369(9558):306\u201318.","journal-title":"Lancet"},{"issue":"1","key":"1436_CR5","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1148\/radiol.10092373","volume":"258","author":"HE Westerlaan","year":"2011","unstructured":"Westerlaan HE, van Dijk JM, de Jansen-van DWM JC, Groen RJ, Mooij JJ, et al. Intracranial aneurysms in patients with subarachnoid hemorrhage: CT angiography as a primary examination tool for diagnosis\u2013systematic review and meta-analysis. Radiology. 2011;258(1):134\u201345.","journal-title":"Radiology"},{"issue":"5","key":"1436_CR6","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.acra.2019.01.013","volume":"26","author":"M Hori","year":"2019","unstructured":"Hori M, Fujita S. Risk Assessment of Intracranial aneurysms with MRI. Acad Radiol. 2019;26(5):674\u20135.","journal-title":"Acad Radiol"},{"issue":"5","key":"1436_CR7","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1161\/01.STR.31.5.1054","volume":"31","author":"T Ingall","year":"2000","unstructured":"Ingall T, Asplund K, Mahonen M, Bonita R. A multinational comparison of subarachnoid hemorrhage epidemiology in the WHO MONICA stroke study. Stroke. 2000;31(5):1054\u201361.","journal-title":"Stroke"},{"issue":"1","key":"1436_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1177\/0846537120954293","volume":"72","author":"H Kaka","year":"2021","unstructured":"Kaka H, Zhang E, Khan N. Artificial Intelligence and Deep Learning in Neuroradiology: exploring the New Frontier. Can Assoc Radiol J. 2021;72(1):35\u201344.","journal-title":"Can Assoc Radiol J"},{"issue":"1","key":"1436_CR9","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12880-024-01347-9","volume":"24","author":"Z Zhou","year":"2024","unstructured":"Zhou Z, Jin Y, Ye H, Zhang X, Liu J, Zhang W. Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review. BMC Med Imaging. 2024;24(1):164.","journal-title":"BMC Med Imaging"},{"issue":"3","key":"1436_CR10","doi-asserted-by":"publisher","first-page":"373","DOI":"10.3174\/ajnr.A6468","volume":"41","author":"Z Shi","year":"2020","unstructured":"Shi Z, Hu B, Schoepf UJ, Savage RH, Dargis DM, Pan CW, et al. Artificial Intelligence in the management of Intracranial aneurysms: current status and future perspectives. AJNR Am J Neuroradiol. 2020;41(3):373\u20139.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"8","key":"1436_CR11","first-page":"1571","volume":"16","author":"RA Alberico","year":"1995","unstructured":"Alberico RA, Patel M, Casey S, Jacobs B, Maguire W, Decker R. Evaluation of the circle of Willis with three-dimensional CT angiography in patients with suspected intracranial aneurysms. AJNR Am J Neuroradiol. 1995;16(8):1571\u20138. discussion 1579-80.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"3","key":"1436_CR12","first-page":"439","volume":"17","author":"JK Hope","year":"1996","unstructured":"Hope JK, Wilson JL, Thomson FJ. Three-dimensional CT angiography in the detection and characterization of intracranial berry aneurysms. AJNR Am J Neuroradiol. 1996;17(3):439\u201345.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"Pt 2","key":"1436_CR13","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1093\/brain\/123.2.205","volume":"123","author":"JM Wardlaw","year":"2000","unstructured":"Wardlaw JM, White PM. The detection and management of unruptured intracranial aneurysms. Brain. 2000;123(Pt 2):205\u201321.","journal-title":"Brain"},{"issue":"4","key":"1436_CR14","first-page":"1522","volume":"27","author":"M Zeynal","year":"2023","unstructured":"Zeynal M, Yalcin A. Before emergency aneurysm surgery, CTA or DSA? A single center experience. Eur Rev Med Pharmacol Sci. 2023;27(4):1522\u20137.","journal-title":"Eur Rev Med Pharmacol Sci"},{"issue":"8","key":"1436_CR15","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500\u201310.","journal-title":"Nat Rev Cancer"},{"issue":"Suppl 3","key":"1436_CR16","doi-asserted-by":"publisher","first-page":"S201","DOI":"10.1016\/j.acra.2021.06.013","volume":"29","author":"O Alwalid","year":"2022","unstructured":"Alwalid O, Long X, Xie M, Han P. Artificial Intelligence Applications in Intracranial Aneurysm: achievements, challenges and opportunities. Acad Radiol. 2022;29(Suppl 3):S201\u201314.","journal-title":"Acad Radiol"},{"issue":"4","key":"1436_CR17","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"JG Lee","year":"2017","unstructured":"Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in Medical Imaging: General Overview. Korean J Radiol. 2017;18(4):570\u201384.","journal-title":"Korean J Radiol"},{"issue":"1","key":"1436_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","volume":"25","author":"J He","year":"2019","unstructured":"He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30\u20136.","journal-title":"Nat Med"},{"issue":"7","key":"1436_CR19","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113\u201331.","journal-title":"Radiographics"},{"issue":"4","key":"1436_CR20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.neurad.2021.05.001","volume":"49","author":"O Shafaat","year":"2022","unstructured":"Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, et al. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol. 2022;49(4):343\u201351.","journal-title":"J Neuroradiol"},{"key":"1436_CR21","doi-asserted-by":"publisher","first-page":"110169","DOI":"10.1016\/j.ejrad.2022.110169","volume":"149","author":"X Wei","year":"2022","unstructured":"Wei X, Jiang J, Cao W, Yu H, Deng H, Chen J, et al. Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography. Eur J Radiol. 2022;149:110169.","journal-title":"Eur J Radiol"},{"issue":"1","key":"1436_CR22","doi-asserted-by":"publisher","first-page":"4829","DOI":"10.1038\/s41467-020-18606-2","volume":"11","author":"F Fu","year":"2020","unstructured":"Fu F, Wei J, Zhang M, Yu F, Xiao Y, Rong D, et al. Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network. Nat Commun. 2020;11(1):4829.","journal-title":"Nat Commun"},{"issue":"10","key":"1436_CR23","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1136\/neurintsurg-2020-015824","volume":"12","author":"H Jin","year":"2020","unstructured":"Jin H, Geng J, Yin Y, Hu M, Yang G, Xiang S, et al. Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network. J Neurointerv Surg. 2020;12(10):1023\u20137.","journal-title":"J Neurointerv Surg"},{"issue":"1","key":"1436_CR24","first-page":"014006","volume":"6","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK. Recurrent residual U-Net for medical image segmentation. J Med Imaging (Bellingham). 2019;6(1):014006.","journal-title":"J Med Imaging (Bellingham)"},{"issue":"1","key":"1436_CR25","doi-asserted-by":"publisher","first-page":"6090","DOI":"10.1038\/s41467-020-19527-w","volume":"11","author":"Z Shi","year":"2020","unstructured":"Shi Z, Miao C, Schoepf UJ, Savage RH, Dargis DM, Pan C, et al. A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat Commun. 2020;11(1):6090.","journal-title":"Nat Commun"},{"key":"1436_CR26","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1080\/02841850802133386","volume":"434","author":"SI Seldinger","year":"2008","unstructured":"Seldinger SI. Catheter replacement of the needle in percutaneous arteriography. A new technique. Acta Radiol Suppl (Stockholm). 2008;434:47\u201352.","journal-title":"Acta Radiol Suppl (Stockholm)"},{"issue":"1","key":"1436_CR27","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3174\/ajnr.A5911","volume":"40","author":"T Sichtermann","year":"2019","unstructured":"Sichtermann T, Faron A, Sijben R, Teichert N, Freiherr J, Wiesmann M. Deep learning-based detection of intracranial aneurysms in 3D TOF-MRA. AJNR Am J Neuroradiol. 2019;40(1):25\u201332.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"2","key":"1436_CR28","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1097\/00004424-199002000-00006","volume":"25","author":"KS Berbaum","year":"1990","unstructured":"Berbaum KS, Franken EJ, Dorfman DD, Rooholamini SA, Kathol MH, Barloon TJ, et al. Satisfaction of search in diagnostic radiology. Invest Radiol. 1990;25(2):133\u201340.","journal-title":"Invest Radiol"},{"issue":"1","key":"1436_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.jvs.2019.12.026","volume":"72","author":"J Raffort","year":"2020","unstructured":"Raffort J, Adam C, Carrier M, Ballaith A, Coscas R, Jean-Baptiste E, et al. Artificial intelligence in abdominal aortic aneurysm. J Vasc Surg. 2020;72(1):321\u2013e3331.","journal-title":"J Vasc Surg"},{"issue":"2","key":"1436_CR30","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3348\/kjr.2020.0313","volume":"22","author":"Y Yu","year":"2021","unstructured":"Yu Y, Gao Y, Wei J, Liao F, Xiao Q, Zhang J, et al. A three-dimensional deep convolutional neural network for automatic segmentation and diameter measurement of type B aortic dissection. Korean J Radiol. 2021;22(2):168\u201378.","journal-title":"Korean J Radiol"},{"key":"1436_CR31","doi-asserted-by":"publisher","first-page":"109424","DOI":"10.1016\/j.ejrad.2020.109424","volume":"134","author":"J Rueckel","year":"2021","unstructured":"Rueckel J, Reidler P, Fink N, Sperl J, Geyer T, Fabritius MP, et al. Artificial intelligence assistance improves reporting efficiency of thoracic aortic aneurysm CT follow-up. Eur J Radiol. 2021;134:109424.","journal-title":"Eur J Radiol"},{"issue":"1","key":"1436_CR32","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1186\/s12880-024-01288-3","volume":"24","author":"S Gao","year":"2024","unstructured":"Gao S, Xu Z, Kang W, Lv X, Chu N, Xu S, et al. Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors\u2019 evaluation in lung cancer screening. BMC Med Imaging. 2024;24(1):141.","journal-title":"BMC Med Imaging"},{"issue":"1","key":"1436_CR33","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neurad.2022.03.005","volume":"50","author":"F Claux","year":"2023","unstructured":"Claux F, Baudouin M, Bogey C, Rouchaud A. Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net. J Neuroradiol. 2023;50(1):9\u201315.","journal-title":"J Neuroradiol"},{"issue":"6","key":"1436_CR34","doi-asserted-by":"publisher","first-page":"e195600","DOI":"10.1001\/jamanetworkopen.2019.5600","volume":"2","author":"A Park","year":"2019","unstructured":"Park A, Chute C, Rajpurkar P, Lou J, Ball RL, Shpanskaya K, et al. Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet Model. JAMA Netw Open. 2019;2(6):e195600.","journal-title":"JAMA Netw Open"},{"issue":"5","key":"1436_CR35","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.jocn.2009.09.015","volume":"17","author":"B Franklin","year":"2010","unstructured":"Franklin B, Gasco J, Uribe T, VonRitschl RH, Hauck E. Diagnostic accuracy and inter-rater reliability of 64-multislice 3D-CTA compared to intra-arterial DSA for intracranial aneurysms. J Clin Neurosci. 2010;17(5):579\u201383.","journal-title":"J Clin Neurosci"},{"issue":"3","key":"1436_CR36","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1111\/jon.12712","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang X, Benson JC, Jagadeesan B, McKinney A. Giant cerebral aneurysms: comparing CTA, MRA, and Digital Subtraction Angiography assessments. J Neuroimaging. 2020;30(3):335\u201341.","journal-title":"J Neuroimaging"},{"issue":"3","key":"1436_CR37","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3348\/kjr.2019.0025","volume":"20","author":"DW Kim","year":"2019","unstructured":"Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: results from recently published papers. Korean J Radiol. 2019;20(3):405\u201310.","journal-title":"Korean J Radiol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01436-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01436-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01436-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:02:33Z","timestamp":1727827353000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01436-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1436"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01436-9","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]},"assertion":[{"value":"21 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The Clinical Medical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University approved this retrospective study. The Clinical Medical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University waived the requirement for written informed consent. All patients undergoing head and neck angiography had signed informed consent for undergoing the CTA examination. The study was performed per the ethical standards as laid down in the 1964 Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"261"}}