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

Figures

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  Figure 1. Comparison of the FEA-based approach and the ML-based approach. FEA: finite element analysis. ML: machine learning. TAV: transcatheter aortic valve.

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  Figure 2. The process of current FEA-based analysis for TAV design. 2D: two-dimensional; 3D: three-dimensional. Examples of 2D designs are shown in (a). Each 2D design is converted to a 2D mesh shown in (b). The SSL designs are shown in (c). The assembled 3D leaflets are shown in (d). The valve closure simulation result is shown in (e).

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  Figure 3. The extreme leaflet shapes visualized in the space of valve design parameters.

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  Figure 4. The structure and data flow of the autoencoder-based ML-models.

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  Figure 5. The structure and data flow of an autoencoder.

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  Figure 6. The structure and data flow of each direct ML-model.

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  Figure 7. Comparison of deformed shape (i.e., mesh, distance errors are color-coded) and stress distributions (three components: S11, S22, S12) from FEA and ML for one representative example. The relative stress error at node-n is defined as S˜(n)/|max{S}−min{S}|, where S˜(n) is from the ML model.

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  Figure 8. The top row shows the MeanE (kPa) distributions/histograms of S11, S12, and S22 predicted by the 1st ML-model-a. The bottom row shows the MeanE (kPa) distributions of S11, S12, and S22 predicted by using the mean distributions.

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  Figure 9. The left plot shows the MeanE (mm) distribution /histogram of shapes predicted by the 1st ML-model-a. The right plot shows the MeanE (mm) distribution of shapes predicted by using the mean shape.