A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
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DOI：
10.1098/rsif.2017.0844
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发表时间：
2018-01-01
影响因子：
3.9
通讯作者：
Sun, Wei
中科院分区：
文献类型：
--
作者：
Liang, Liang;Liu, Minliang;Sun, Wei
结构有限元分析（FEA）已被广泛用于研究人体组织和器官的生物力学，以及组织-医疗器械相互作用和治疗策略。然而，患者特定的FEA模型通常需要复杂的程序来设置，并且需要长的计算时间来获得最终的模拟结果，从而阻止了在时间敏感的临床应用中向临床医生及时反馈。在这项研究中，通过使用机器学习技术，我们开发了一个深度学习（DL）模型来直接估计主动脉的应力分布。DL模型的设计和训练采用FEA的输入并直接输出主动脉壁应力分布，绕过FEA计算过程。训练后的DL模型能够预测应力分布，Von Mises应力分布和峰值Von Mises应力的平均误差分别为0.492%和0.891%。据我们所知，这项研究标志着第一项研究，证明了使用DL技术作为有限元应力分析的快速，准确的替代品的可行性和巨大潜力。
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.