Filipovic Nenad, Ivanovic Milos, Krstajic Damjan, Kojic Milos
Faculty of Mechanical Engineering, University of Kragujevac, Kragujevac, Serbia.
IEEE Trans Inf Technol Biomed. 2011 Mar;15(2):189-94. doi: 10.1109/TITB.2010.2096541. Epub 2010 Dec 3.
Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.
血管的几何变化，即动脉瘤，在人类中经常发生，可能会导致灾难性后果。然后，血流会受到极大影响，作用于动脉壁的血液血流动力学相互作用力也会受到影响。这些力是血管壁破裂的原因。血液与血管壁相互作用的一个机械量特征是壁面剪应力，它对内皮细胞行为也有直接的生理影响。因此，当动脉瘤形成时，深入了解血流和剪应力分布非常重要，这有助于将机械条件与血管病理变化的发病机制联系起来。这种深入了解还可以进一步帮助改善对心血管疾病发展的预防。计算流体动力学（CFD）通常被用作一种工具，以生成有或没有动脉瘤的血管内机械条件的结果。然而，动脉瘤非常具有个体特异性，通过繁琐且耗时的计算模型生成过程以及大量计算，才能从CFD分析中获得可靠结果。为了使CFD分析高效且适用于未来的日常临床实践，我们在此采用了数据挖掘（DM）技术。重点是将CFD和DM方法结合起来，以估计在规定几何变化下腹主动脉瘤（AAA）中的壁面剪应力。此外，由于创建机器学习数据需要进行数千次CFD运行，因此在网格基础设施上进行计算以提高效率。我们使用了几种DM技术，发现与基于真实患者数据的完整CFD模型结果相比，我们的DM模型能够很好地预测AAA处的剪应力。
