A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves

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2019-12-06
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Balu, Aditya
Nallagonda, Sahiti
Xu, Fei
Sarkar, Soumik
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Krishnamurthy, Adarsh
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Hsu, Ming-Chen
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Mechanical Engineering
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Abstract

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.

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This article is published as Balu, Aditya, Sahiti Nallagonda, Fei Xu, Adarsh Krishnamurthy, Ming-Chen Hsu, and Soumik Sarkar. "A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves." Scientific Reports 9 (2019): 18560. DOI: 10.1038/s41598-019-54707-9. Posted with permission.

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Tue Jan 01 00:00:00 UTC 2019
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