A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.
A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.
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DOI：
10.1007/s10237-017-0903-9
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发表时间：
2017-10
影响因子：
3.5
通讯作者：
Sun W
中科院分区：
文献类型：
--
作者：
Liang L;Liu M;Martin C;Elefteriades JA;Sun W
在选择性修复升主动脉瘤(ASAA)的临床决策中，主动脉的几何特征与患者破裂的风险有关。以往的研究主要集中在直观的几何特征(如直径和曲率)与墙体应力之间的关系上。本工作探讨了用机器学习方法建立形状特征与有限元分析预测的ASAA破裂风险之间的联系的可行性，并可能作为与较长的模拟时间和数值收敛问题相关的有限元分析的更快替代物。该方法包括四个主要步骤：(1)从ASAA患者的临床三维CT图像构建统计形状模型(SSM)；(2)生成典型动脉瘤形状的数据集，并获得定义为收缩压除以破裂压(破裂由阈值标准确定)的有限元分析预测风险分数；(3)使用分类器和回归分析建立形状特征与风险之间的关系；以及(4)在交叉验证中对这种关系进行评估。结果表明，支持向量机参数可以作为较强的形状特征，使风险分值的预测与有限元分析一致，支持向量机的平均风险分类准确率为95.58%，支持向量回归的平均回归误差为0.0332，而直观的几何特征的预测效果相对较弱。与有限元分析相比，这种机器学习方法的速度要快很多。在我们未来的研究中，材料特性和厚度不均匀将被纳入到模型和学习算法中，这可能会导致一个实用的系统用于临床应用。
Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g. diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.
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DOI：
10.1002/nme.1807
发表时间：
2007-02-05
影响因子：
2.9
作者：
Lu, Jia;Zhou, Xianlian;Raghavan, Madhavan L.
通讯作者：
Raghavan, Madhavan L.
影响因子：
5.5
作者：
Barrett, JF;Keat, N
通讯作者：
Keat, N
影响因子：
2.5
作者：
Bommes, David;Levy, Bruno;Zorin, Denis
通讯作者：
Zorin, Denis
影响因子：
4.3
作者：
Hua, J;Mower, WR
通讯作者：
Mower, WR
影响因子：
4.3
作者：
Fillinger, MF;Racusin, J;Rzucidlo, EM
通讯作者：
Rzucidlo, EM