A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves
A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves
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DOI：
10.1038/s41598-019-54707-9
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发表时间：
2019-12-06
影响因子：
4.6
通讯作者：
Sarkar, Soumik
中科院分区：
文献类型：
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作者：
Balu, Aditya;Nallagonda, Sahiti;Sarkar, Soumik
生物瓣膜(BHV)是常用的心脏瓣膜替代物，但容易发生疲劳失效，直接从医学图像中估计其剩余寿命是困难的。对阀门性能进行分析，可以更好地指导个性化阀门的设计。然而，这样的分析通常是计算密集型的。在这项工作中，我们引入了基于深度学习的有限元分析(DLFEA)的概念，以直接从模拟中了解生物人工主动脉瓣的变形生物力学。所提出的DL框架可以省去耗时的生物力学模拟，同时以相同的保真度预测瓣膜变形。我们给出的统计结果证明了DLFEA框架的高性能以及该框架在预测生物人工主动脉瓣畸形方面的适用性。随着进一步的发展，该工具可以为外科生物瓣的设计提供快速的决策支持。最终，这一框架可以扩展到其他BHV，并改善患者护理。
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.