TY - JOUR
T1 - A deep learning application to approximate the geometric orifice and coaptation areas of the polymeric heart valves under time – varying transvalvular pressure
AU - Gulbulak, Utku
AU - Gecgel, Ozhan
AU - Ertas, Atila
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/5
Y1 - 2021/5
N2 - Machine learning and deep learning frameworks have been presented as a substitute for lengthy computational analysis, such as finite element analysis, computational fluid dynamics, and fluid-structure interaction. In this study, our objective was to apply a deep learning framework to predict the geometric orifice (GOA) and the coaptation areas (CA) of the polymeric heart valves under the time-varying transvalvular pressure. 377 different valve geometries were generated by changing the control coordinates of the attachment and the belly curve. The GOA and the CA values were obtained at the maximum and the minimum transvalvular pressure, respectively. The results showed that the applied framework can accurately predict the GOA and the CA despite being trained with a relatively smaller data set. The presented framework can reduce the required time of the lengthy FE frameworks.
AB - Machine learning and deep learning frameworks have been presented as a substitute for lengthy computational analysis, such as finite element analysis, computational fluid dynamics, and fluid-structure interaction. In this study, our objective was to apply a deep learning framework to predict the geometric orifice (GOA) and the coaptation areas (CA) of the polymeric heart valves under the time-varying transvalvular pressure. 377 different valve geometries were generated by changing the control coordinates of the attachment and the belly curve. The GOA and the CA values were obtained at the maximum and the minimum transvalvular pressure, respectively. The results showed that the applied framework can accurately predict the GOA and the CA despite being trained with a relatively smaller data set. The presented framework can reduce the required time of the lengthy FE frameworks.
KW - Coaptation area
KW - Deep learning
KW - Finite element
KW - Geometric orifice area
KW - Machine learning
KW - Polymeric heart valves
UR - http://www.scopus.com/inward/record.url?scp=85101366012&partnerID=8YFLogxK
U2 - 10.1016/j.jmbbm.2021.104371
DO - 10.1016/j.jmbbm.2021.104371
M3 - Article
C2 - 33610020
AN - SCOPUS:85101366012
VL - 117
JO - Journal of the Mechanical Behavior of Biomedical Materials
JF - Journal of the Mechanical Behavior of Biomedical Materials
SN - 1751-6161
M1 - 104371
ER -