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

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. PMID: 33610020 [PubMed - as supplied by publisher]
Source: Journal of the Mechanical Behavior of Biomedical Materials - Category: Materials Science Authors: Tags: J Mech Behav Biomed Mater Source Type: research