Artificial heart valves, used to replace diseased human heart valves, are life-saving medical\ndevices. Currently, at the device development stage, new artificial valves are primarily assessed\nthrough time-consuming and expensive benchtop tests or animal implantation studies. Computational\nstress analysis using the finite element (FE) method presents an attractive alternative to physical\ntesting. However, FE computational analysis requires a complex process of numeric modeling and\nsimulation, as well as in-depth engineering expertise. In this proof of concept study, our objective\nwas to develop machine learning (ML) techniques that can estimate the stress and deformation of\na transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep\nneural networks were developed and compared: the autoencoder-based ML-models and the direct\nML-models. The ML-models were evaluated through Monte Carlo cross validation. From the\nresults, both proposed deep neural networks could accurately estimate the deformed geometry of\nthe TAV leaflets and the associated stress distributions within a second, with the direct ML-models\n(ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study,\nthe proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for\nfuture TAV design.
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