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Open AccessArticle

A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

by Liang Liang 1,* and Bill Sun 2
1
Department of Computer Science, University of Miami, Coral Gables, FL 33146, USA
2
Walton High School, Marietta, GA 30062, USA
*
Author to whom correspondence should be addressed.
Bioengineering 2019, 6(4), 104; https://doi.org/10.3390/bioengineering6040104
Received: 24 September 2019 / Revised: 31 October 2019 / Accepted: 4 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Implantable Medical Devices)
Artificial heart valves, used to replace diseased human heart valves, are life-saving medical devices. Currently, at the device development stage, new artificial valves are primarily assessed through time-consuming and expensive benchtop tests or animal implantation studies. Computational stress analysis using the finite element (FE) method presents an attractive alternative to physical testing. However, FE computational analysis requires a complex process of numeric modeling and simulation, as well as in-depth engineering expertise. In this proof of concept study, our objective was to develop machine learning (ML) techniques that can estimate the stress and deformation of a transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep neural networks were developed and compared: the autoencoder-based ML-models and the direct ML-models. The ML-models were evaluated through Monte Carlo cross validation. From the results, both proposed deep neural networks could accurately estimate the deformed geometry of the TAV leaflets and the associated stress distributions within a second, with the direct ML-models (ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study, the proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for future TAV design. View Full-Text
Keywords: artificial heart valve; transcatheter aortic valve; finite element analysis; machine learning; deep neural network artificial heart valve; transcatheter aortic valve; finite element analysis; machine learning; deep neural network
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Liang, L.; Sun, B. A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis. Bioengineering 2019, 6, 104.

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