Abstract
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.
Highlights
In the past 15 years, transcatheter valve devices have experienced explosive growth and development in the field of transcatheter cardiovascular therapy
We have recently demonstrated that machine learning techniques can be applied in a variety of biomechanical analyses [40,41,42,43,44,45,46], which include the reconstruction of the native aortic valve from three-dimensional (3D) computed tomography images [43] and the estimation of stress distribution from finite element analysis (FEA) [44]
Given valve design parameters as the input, our goal was that the machine learning (ML)-models (i.e., deep neural networks (DNNs)), once trained on existing FEA results, could accurately estimate the deformed geometry of the transcatheter aortic valve (TAV) leaflets and the associated stress distributions, within a second
Summary
In the past 15 years, transcatheter valve devices have experienced explosive growth and development in the field of transcatheter cardiovascular therapy. Transcatheter aortic valve replacement (TAVR), first implanted in a human in 2002 by Alan Cribier [1], has been performed for over 350,000 patients in over 70 countries [2]. A myriad of new transcatheter valve devices with innovative designs have been developed and undergone pre-clinical and clinical trials [5,6]. Artificial heart valves are FDA Class III medical devices that require significant investment and years of development from their concept designs to commercialization. In such a time-consuming and expensive process, it is paramount that any faulty valve design can be detected and corrected as soon as possible before proceeding to pre-clinical and clinical trials
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