{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:41:14Z","timestamp":1792737674740,"version":"4.5.0"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004737","name":"National Health Research Institutes","doi-asserted-by":"publisher","award":["NHRI-EX113-11324EI"],"award-info":[{"award-number":["NHRI-EX113-11324EI"]}],"id":[{"id":"10.13039\/501100004737","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ejrad.2026.113189","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T23:38:57Z","timestamp":1787701137000},"page":"113189","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning versus radiologists for acute aortic dissection on CT: A systematic review and network meta-analysis"],"prefix":"10.1016","volume":"205","author":[{"given":"Ting-Wei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-8841","authenticated-orcid":false,"given":"Jia-Sheng","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho-Ren","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-Ting","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Neng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3928-0164","authenticated-orcid":false,"given":"Yung-Tsai","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Hsiang","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guan-Hua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Ming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Hsian","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-Hsuan","family":"Tzeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Te","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.113189_b0005","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.athoracsur.2024.01.021","article-title":"EACTS\/STS guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ","volume":"118","author":"Czerny","year":"2024","journal-title":"Ann. Thorac. Surg."},{"key":"10.1016\/j.ejrad.2026.113189_b0010","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1007\/s00330-020-07213-w","article-title":"Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT","volume":"31","author":"Hata","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.113189_b0015","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2021.762958","article-title":"Advanced warning of aortic dissection on non-contrast CT: the combination of deep learning and morphological characteristics","volume":"8","author":"Yi","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.ejrad.2026.113189_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102145","article-title":"A deep learning algorithm using contrast-enhanced computed tomography images for segmentation and rapid automatic detection of aortic dissection","volume":"62","author":"Cheng","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.ejrad.2026.113189_b0025","doi-asserted-by":"crossref","first-page":"7365","DOI":"10.21037\/qims-24-533","article-title":"A deep learning algorithm for the detection of aortic dissection on non-contrast-enhanced computed tomography via the identification and segmentation of the true and false lumens of the aorta","volume":"14","author":"Cheng","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.ejrad.2026.113189_b0030","article-title":"Improved detection of aortic dissection in non-contrast-enhanced chest CT using an attention-based deep learning model","volume":"10","author":"Dong","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.ejrad.2026.113189_b0035","article-title":"ADSeg: a flap-attention-based deep learning approach for aortic dissection segmentation","volume":"4","author":"Xiang","year":"2023","journal-title":"Patterns (n y)"},{"key":"10.1016\/j.ejrad.2026.113189_b0040","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1007\/s00330-021-08370-2","article-title":"Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography","volume":"32","author":"Huang","year":"2022","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.113189_b0045","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.1016\/j.ejrad.2026.113189_b0050","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.ejrad.2026.113189_b0055","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.ejrad.2026.113189_b0060","doi-asserted-by":"crossref","unstructured":"Sch\u00fcnemann HJ, Mustafa RA, Brozek J, et al. GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy. J Clin Epidemiol 2020;122:129-141. doi: 10.1016\/j.jclinepi.2019.12.020.","DOI":"10.1016\/j.jclinepi.2019.12.020"},{"key":"10.1016\/j.ejrad.2026.113189_b0065","doi-asserted-by":"crossref","unstructured":"Sch\u00fcnemann HJ, Mustafa RA, Brozek J, et al. GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables. J Clin Epidemiol 2020;122:142-152. doi: 10.1016\/j.jclinepi.2019.12.021.","DOI":"10.1016\/j.jclinepi.2019.12.021"},{"key":"10.1016\/j.ejrad.2026.113189_b0070","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1186\/s13690-021-00747-5","article-title":"metadta: a Stata command for meta-analysis and meta-regression of diagnostic test accuracy data \u2014 a tutorial","volume":"80","author":"Nyaga","year":"2022","journal-title":"Arch. Public Health"},{"key":"10.1016\/j.ejrad.2026.113189_b0145","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ae3b00","article-title":"Adaptive geometric-attention multi-task framework with knowledge distillation for aortic dissection detection in non-contrast CT","volume":"71","author":"Zhang","year":"2026","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.ejrad.2026.113189_b0130","doi-asserted-by":"crossref","first-page":"3832","DOI":"10.1038\/s41591-025-03916-z","article-title":"AI-based diagnosis of acute aortic syndrome from noncontrast CT","volume":"31","author":"Hu","year":"2025","journal-title":"Nat. Med."},{"key":"10.1016\/j.ejrad.2026.113189_b0155","doi-asserted-by":"crossref","first-page":"8359","DOI":"10.21037\/qims-2024-2807","article-title":"An innovative bimodal computed tomography data-driven deep learning model for predicting aortic dissection: a multi-center study","volume":"15","author":"Li","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.ejrad.2026.113189_b0160","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.ejvs.2025.08.054","article-title":"Deep learning application of YOLOv8 for aortic dissection screening using non-contrast computed tomography","volume":"71","author":"Tang","year":"2026","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"10.1016\/j.ejrad.2026.113189_b0135","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/s13244-025-02098-z","article-title":"Segmentation-model-based framework to detect aortic dissection on non-contrast CT images: a retrospective study","volume":"16","author":"Wang","year":"2025","journal-title":"Insights Imaging"},{"key":"10.1016\/j.ejrad.2026.113189_b0080","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s13239-025-00787-w","article-title":"A fully automatic pipeline of identification, segmentation, and subtyping of aortic dissection from CT angiography","volume":"16","author":"Zhuang","year":"2025","journal-title":"Cardiovasc. Eng. Technol."},{"key":"10.1016\/j.ejrad.2026.113189_b0150","doi-asserted-by":"crossref","first-page":"866","DOI":"10.3390\/diagnostics14090866","article-title":"Artificial intelligence provides accurate quantification of thoracic aortic enlargement and dissection in chest CT","volume":"14","author":"Fink","year":"2024","journal-title":"Diagnostics (basel)"},{"key":"10.1016\/j.ejrad.2026.113189_b0085","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.3390\/diagnostics14131332","article-title":"Automatic segmentation of type a aortic dissection on computed tomography images using deep learning approach","volume":"14","author":"Guo","year":"2024","journal-title":"Diagnostics (basel)"},{"key":"10.1016\/j.ejrad.2026.113189_b0090","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s41747-023-00342-z","article-title":"Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection","volume":"7","author":"Kes\u00e4vuori","year":"2023","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.ejrad.2026.113189_b0095","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1007\/s00330-021-08130-2","article-title":"Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach","volume":"32","author":"Sieren","year":"2022","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.113189_b0100","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/TMI.2022.3150005","article-title":"Segmentation of the true lumen of aorta dissection via morphology-constrained stepwise deep mesh regression","volume":"41","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ejrad.2026.113189_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101931","article-title":"Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification","volume":"69","author":"Chen","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ejrad.2026.113189_b0110","first-page":"3912","article-title":"Deep learning-based 3D segmentation of true lumen, false lumen, and false lumen thrombosis in type-B aortic dissection","volume":"2021","author":"Wobben","year":"2021","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.ejrad.2026.113189_b0140","doi-asserted-by":"crossref","first-page":"5177","DOI":"10.1109\/JBHI.2022.3190293","article-title":"A cascaded multi-task generative framework for detecting aortic dissection on 3D non-contrast-enhanced computed tomography","volume":"26","author":"Xiong","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.ejrad.2026.113189_b0115","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2021.732711","article-title":"ImageTBAD: a 3D computed tomography angiography image dataset for automatic segmentation of type-B aortic dissection","volume":"12","author":"Yao","year":"2021","journal-title":"Front. Physiol."},{"key":"10.1016\/j.ejrad.2026.113189_b0120","doi-asserted-by":"crossref","DOI":"10.1148\/ryct.2020190179","article-title":"CT-based true- and false-lumen segmentation in type B aortic dissection using machine learning","volume":"2","author":"Hahn","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"10.1016\/j.ejrad.2026.113189_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2019.108713","article-title":"Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning","volume":"121","author":"Cao","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113189_b0075","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i03","article-title":"Conducting meta-analyses in R with the metafor package","volume":"36","author":"Viechtbauer","year":"2010","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.ejrad.2026.113189_b0165","article-title":"Performance of image-based deep learning models for aortic dissection segmentation and diagnosis: a systematic review and meta-analysis","volume":"13","author":"Zhao","year":"2026","journal-title":"Front. Cardiovasc. Med."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26005371?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26005371?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:32:35Z","timestamp":1792737155000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26005371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":33,"alternative-id":["S0720048X26005371"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113189","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning versus radiologists for acute aortic dissection on CT: A systematic review and network meta-analysis","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113189","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113189"}}