This study was performed to determine the relations between the features of wall shear stress and aneurysm rupture. For this purpose, visual data mining was performed in unsteady blood flow simulation data for an aortic aneurysm. The time-series data of wall shear stress given at each grid point were converted to spatial and temporal indices, and the grid points were sorted using a self-organizing map based on the similarity of these indices. Next, the results of cluster analysis were mapped onto the real space of the aortic aneurysm to specify the regions that may lead to aneurysm rupture. With reference to previous reports regarding aneurysm rupture, the visual data mining suggested specific hemodynamic features that cause aneurysm rupture.Graphical Abstract

中文翻译：

---

主动脉瘤非稳态血流场可视化数据挖掘的实现

进行这项研究是为了确定壁剪应力特征与动脉瘤破裂之间的关系。为此，对主动脉瘤的不稳定血流模拟数据进行了可视化数据挖掘。将每个网格点给出的墙体剪应力的时间序列数据转换为空间和时间指标，并根据这些指标的相似性使用自组织图对网格点进行排序。接下来，聚类分析的结果被映射到主动脉瘤的真实空间，以指定可能导致动脉瘤破裂的区域。参考以往关于动脉瘤破裂的报道，可视化数据挖掘提出了导致动脉瘤破裂的特定血流动力学特征。图形摘要