A deep learning application to approximate the geometric orifice and coaptation areas of the polymeric heart valves under time – varying transvalvular pressure

Utku Gulbulak, Ozhan Gecgel, Atila Ertas

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number104371
JournalJournal of the Mechanical Behavior of Biomedical Materials
Volume117
DOIs
StatePublished - May 2021

Keywords

  • Coaptation area
  • Deep learning
  • Finite element
  • Geometric orifice area
  • Machine learning
  • Polymeric heart valves

Fingerprint

Dive into the research topics of 'A deep learning application to approximate the geometric orifice and coaptation areas of the polymeric heart valves under time – varying transvalvular pressure'. Together they form a unique fingerprint.

Cite this