Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

Machine learning–aided exploration of relationship between strength and elastic properties in... Machine learning was applied to classify tension‐strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of features were used as the basis for classification. The first was the constitutive parameters fitted from the tension‐strain data, and the second was geometric parameters extracted from the tension‐strain curve. Based on the importance scores provided by the machine learning, implications of some features were interrogated. It was found that (1) the value of a constitutive parameter is nearly the same for all members in the rupture group and (2) the strength correlates strongly with a tension in the early phase of response as well as with the end stiffness. The study suggests that the strength, which is not available without rupturing the tissue, may be indirectly inferred from prerupture response features. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal for Numerical Methods in Biomedical Engineering Wiley

Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
2040-7939
eISSN
2040-7947
D.O.I.
10.1002/cnm.2977
Publisher site
See Article on Publisher Site

Abstract

Machine learning was applied to classify tension‐strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of features were used as the basis for classification. The first was the constitutive parameters fitted from the tension‐strain data, and the second was geometric parameters extracted from the tension‐strain curve. Based on the importance scores provided by the machine learning, implications of some features were interrogated. It was found that (1) the value of a constitutive parameter is nearly the same for all members in the rupture group and (2) the strength correlates strongly with a tension in the early phase of response as well as with the end stiffness. The study suggests that the strength, which is not available without rupturing the tissue, may be indirectly inferred from prerupture response features.

Journal

International Journal for Numerical Methods in Biomedical EngineeringWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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