{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:44:53Z","timestamp":1787676293324,"version":"build-2784847793"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81771165"],"award-info":[{"award-number":["81771165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation","award":["2020A1515010233"],"award-info":[{"award-number":["2020A1515010233"]}]},{"name":"Natural Science Foundation","award":["2018A030313172"],"award-info":[{"award-number":["2018A030313172"]}]},{"name":"Guangzhou Science","award":["201704030032"],"award-info":[{"award-number":["201704030032"]}]},{"name":"Guangzhou Science","award":["201807010010"],"award-info":[{"award-number":["201807010010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and results<\/jats:title><jats:p>A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, n = 139) and testing set (10%, n = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and assessed using the dice coefficient (DC), normalized mean absolute error (NMAE), and root mean square error (RMSE). Our AI platform brought into correspondence with the manual segmentation (DC = 0.86) and the CFD findings (NMAE, 7.8773% \u00b1 4.7144%; RMSE, 0.0098 \u00b1 0.0097), while saving 12000-fold computational cost.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The high-efficiency and robust AI platform can automatically estimate value and distribution of TAWSS in ascending aorta, which may be suitable for clinical applications and provide potential ideas for CFD-based problem solving.<\/jats:p><\/jats:sec>","DOI":"10.1093\/ehjdh\/ztac058","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T06:46:32Z","timestamp":1665729992000},"page":"525-534","source":"Crossref","is-referenced-by-count":11,"title":["An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta"],"prefix":"10.1093","volume":"3","author":[{"given":"Lei","family":"Lv","sequence":"first","affiliation":[{"name":"Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yan Jiang West Road, 510120 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haotian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yan Jiang West Road, 510120 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zonglv","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yan Jiang West Road, 510120 Guangzhou , China"},{"name":"Department of Cardiac Surgery, Guangzhou Women and Children's Medical Center , No. 9 Jinsui Road, 510623 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weike","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yanjiang West Road, 510120 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Hua","sequence":"additional","affiliation":[{"name":"Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yan Jiang West Road, 510120 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2490-7091","authenticated-orcid":false,"given":"Songran","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , No. 107 Yan Jiang West Road, 510120 Guangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/S0025-6196(11)60566-1","article-title":"Acute aortic syndromes and thoracic aortic aneurysm","volume":"84","author":"Ramanath","year":"2009","journal-title":"Mayo Clin Proc"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1093\/eurheartj\/ehu281","article-title":"2014 ESC guidelines on the diagnosis and treatment of aortic diseases: document covering acute and chronic aortic diseases of the thoracic and abdominal aorta of the adult. The task force for the diagnosis and treatment of aortic diseases of the European society of cardiology (ESC)","volume":"35","author":"Erbel","year":"2014","journal-title":"Eur Heart J"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/S0022-5223(03)00608-1","article-title":"Relationship of aortic cross-sectional area to height ratio and the risk of aortic dissection in patients with bicuspid aortic valves","volume":"126","author":"Svensson","year":"2003","journal-title":"J Thorac Cardiov Sur"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","DOI":"10.1115\/1.4037857","article-title":"Impact of patient-specific inflow velocity profile on hemodynamics of the thoracic aorta","volume":"140","author":"Youssefi","year":"2018","journal-title":"J Biomech Eng"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1016\/j.athoracsur.2020.03.017","article-title":"Shear stress and aortic strain associations with biomarkers of ascending thoracic aortic aneurysm","volume":"110","author":"Pasta","year":"2020","journal-title":"Ann Thorac Surg"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511800955","volume-title":"An Introduction to fluid dynamics","author":"Batchelor","year":"2000"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.jcmg.2019.06.018","article-title":"CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study","volume":"13","author":"Tang","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"2023021909293967800_","first-page":"676","article-title":"Effects of arterial geometry on aneurysm growth: three-dimensional computational fluid dynamics study","volume":"101","author":"Hoi","year":"2004","journal-title":"J Neurol Neurosurg"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1016\/j.jacc.2012.11.083","article-title":"Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis","volume":"61","author":"Taylor","year":"2013","journal-title":"J Am Coll Cardiol"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies in medicine","volume":"25","author":"He","year":"2019","journal-title":"Nat Med"},{"key":"2023021909293967800_","article-title":"Prediction of 3D cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning","volume":"4","author":"Li","year":"2021","journal-title":"Commun Biol"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1126\/science.aaw4741","article-title":"Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations","volume":"367","author":"Raissi","year":"2020","journal-title":"Sci"},{"key":"2023021909293967800_","article-title":"Deep learning framework for real-time estimation of in-silico thrombotic risk indices in the left atrial appendage","volume":"12","author":"Ferez","year":"2021","journal-title":"Front Physiol"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2021.733444","article-title":"Prediction of cerebral aneurysm hemodynamics with porous-Medium models of flow-diverting stents via deep learning","volume":"12","author":"Li","year":"2021","journal-title":"Front Physiol"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7126532","article-title":"Wall shear stress estimation of thoracic aortic aneurysm using computational fluid dynamics","volume":"2018","author":"Febina","year":"2018","journal-title":"Comput Math Methods Med"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1148\/radiol.2021204381","article-title":"Relationship of endothelial shear stress with plaque features with coronary CT angiography and vasodilating capability with PET","volume":"300","author":"Kalykakis","year":"2021","journal-title":"Radiology"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1007\/s00330-012-2465-7","article-title":"Haemodynamic imaging of thoracic stent-grafts by computational fluid dynamics (CFD): presentation of a patient-specific method combining magnetic resonance imaging and numerical simulations","volume":"22","author":"Midulla","year":"2012","journal-title":"Eur Radiol"},{"key":"2023021909293967800_","author":"Moenning","year":"2003"},{"key":"2023021909293967800_","first-page":"203","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Med"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1002\/mp.13438","article-title":"U-Net based deep learning bladder segmentation in CT urography","volume":"46","author":"Ma","year":"2019","journal-title":"Med Phys"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1016\/j.jcmg.2019.08.025","article-title":"Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep-learning method","volume":"13","author":"Baskaran","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"2023021909293967800_","first-page":"26","article-title":"Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning","volume":"4","author":"Tieleman","year":"2012"},{"key":"2023021909293967800_","first-page":"77","volume-title":"Proceedings\u201430th IEEE conference on computer vision and pattern recognition, CVPR 2017","author":"Qi","year":"2017"},{"key":"2023021909293967800_","author":"Kingma"},{"key":"2023021909293967800_","first-page":"240","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","DOI":"10.1098\/rsif.2017.0844","article-title":"A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis","volume":"15","author":"Liang","year":"2018","journal-title":"J R Soc Interface"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?\u2014arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci Model Dev"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"H1786","DOI":"10.1152\/ajpheart.00461.2014","article-title":"A longitudinal comparison of hemodynamics and intraluminal thrombus deposition in abdominal aortic aneurysms","volume":"307","author":"Arzani","year":"2014","journal-title":"AM J Physiol-Heart C"},{"key":"2023021909293967800_","first-page":"2062","article-title":"Transient numerical simulation of the right coronary artery originating from the left sinus and the effect of its acute take-off angle on hemodynamics. Quant","volume":"11","author":"Cong","year":"2021","journal-title":"Imaging"},{"key":"2023021909293967800_","first-page":"425","article-title":"Combined CFD and MRI study of blood flow in a human ascending aorta model","volume":"39","author":"Leuprecht","year":"2002","journal-title":"Biorheology"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1093\/ejcts\/ezv427","article-title":"Aortic root haemodynamics following david procedure: numerical analysis of 3-dimensional haemodynamics","volume":"49","author":"Berdajs","year":"2016","journal-title":"Eur J Cardiothorac Surg"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jcct.2021.11.014","article-title":"Patient-specific computer simulation to predict long-term outcomes after transcatheter aortic valve replacement","volume":"16","author":"Dowling","year":"2022","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1016\/j.jbiomech.2016.06.010","article-title":"The influence of anesthesia and fluid-structure interaction on simulated shear stress patterns in the carotid bifurcation of mice","volume":"49","author":"De Wilde","year":"2016","journal-title":"J Biomech"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1161\/01.RES.39.1.58","article-title":"Turbulent blood flow in the ascending aorta of humans with Normal and diseased aortic valves","volume":"39","author":"Stein","year":"1976","journal-title":"Circ Res"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"772142","DOI":"10.3389\/fped.2022.772142","article-title":"A pilot study characterizing flow patterns in the thoracic aorta of patients with connective tissue disease: comparison to age- and gender-matched controls via fluid structure interaction","volume":"10","author":"Camarda","year":"2022","journal-title":"Front Pediatr"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s10439-018-2012-z","article-title":"Coupled morphological-hemodynamic computational analysis of type B aortic dissection: a longitudinal study","volume":"46","author":"Xu","year":"2018","journal-title":"Ann Biomed Eng"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1148\/radiol.2020192173","article-title":"Deep learning single-frame and multiframe super-resolution for cardiac MRI","volume":"295","author":"Masutani","year":"2020","journal-title":"Radiology"},{"key":"2023021909293967800_","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":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1148\/radiol.2021211270","article-title":"Deep learning automated background phase error correction for abdominopelvic 4D flow MRI","volume":"302","author":"You","year":"2022","journal-title":"Radiology"},{"key":"2023021909293967800_","doi-asserted-by":"crossref","first-page":"688","DOI":"10.3348\/kjr.2020.0296","article-title":"Coronary artery lumen segmentation using location-adaptive threshold in coronary computed tomographic angiography: a proof-of-concept","volume":"22","author":"Shin","year":"2021","journal-title":"Korean J Radiol"},{"key":"2023021909293967800_","article-title":"How much data is needed to train a medical image deep learning system to achieve necessary high accuracy","author":"Cho","year":"2015","journal-title":"ArXiv"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztac058\/46873413\/ztac058.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/3\/4\/525\/49255084\/ztac058.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/3\/4\/525\/49255084\/ztac058.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T09:13:55Z","timestamp":1678266835000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/3\/4\/525\/6760872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,14]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,10,14]]},"published-print":{"date-parts":[[2022,12,22]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztac058","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,12,22]]},"published":{"date-parts":[[2022,10,14]]}}}