A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta
A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta
About this item
Full title
Author / Creator
Liang, Liang , Mao, Wenbin and Sun, Wei
Publisher
United States: Elsevier Ltd
Journal title
Language
English
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Publication information
Publisher
United States: Elsevier Ltd
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Scope and Contents
Contents
Numerical analysis methods including finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI) analysis have been used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, for patient-specific computational analysis, co...
Alternative Titles
Full title
A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta
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Author / Creator
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Record Identifier
TN_cdi_proquest_miscellaneous_2322739509
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2322739509
Other Identifiers
ISSN
0021-9290
E-ISSN
1873-2380
DOI
10.1016/j.jbiomech.2019.109544
