Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression
Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression
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DOI：
10.1007/s10237-016-0801-6
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发表时间：
2017-02-01
影响因子：
3.5
通讯作者：
Wall, Wolfgang A.
中科院分区：
文献类型：
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作者：
Biehler, Jonas;Kehl, Sebastian;Wall, Wolfgang A.
腹主动脉瘤（AAA）破裂风险的计算评估需要多个患者特定参数，如壁厚、壁强度和结构特性。不幸的是，许多这些量是不容易获得的，只能通过侵入性程序来确定，从而使计算破裂风险评估过时。本研究探讨了两种不同的方法来预测这些数量使用回归模型结合大量的非侵入性访问，解释变量。我们收集了一个大型数据集，包括使用AAA样本进行的拉伸试验和基于血液分析、患者病史和AAA几何特征的补充患者信息。使用这个独特的数据库，我们利用最先进的贝叶斯回归技术的能力来推断多个感兴趣的数量的概率模型。我们的实验结果的简要介绍后，我们表明，我们可以有效地减少预测的不确定性，在评估几个患者的具体参数，最重要的是在厚度和破坏强度的AAA壁。因此，基于高斯过程的更精细的贝叶斯回归方法始终优于标准线性回归。此外，我们的研究包含了一个比较以前提出的模型的壁强度。
Multiple patient-specific parameters, such as wall thickness, wall strength, and constitutive properties, are required for the computational assessment of abdominal aortic aneurysm (AAA) rupture risk. Unfortunately, many of these quantities are not easily accessible and could only be determined by invasive procedures, rendering a computational rupture risk assessment obsolete. This study investigates two different approaches to predict these quantities using regression models in combination with a multitude of noninvasively accessible, explanatory variables. We have gathered a large dataset comprising tensile tests performed with AAA specimens and supplementary patient information based on blood analysis, the patients medical history, and geometric features of the AAAs. Using this unique database, we harness the capability of state-of-the-art Bayesian regression techniques to infer probabilistic models for multiple quantities of interest. After a brief presentation of our experimental results, we show that we can effectively reduce the predictive uncertainty in the assessment of several patient-specific parameters, most importantly in thickness and failure strength of the AAA wall. Thereby, the more elaborate Bayesian regression approach based on Gaussian processes consistently outperforms standard linear regression. Moreover, our study contains a comparison to a previously proposed model for the wall strength.