Abdominal aortic aneurysm (AAA) is an asymptomatic aortic disease with a survival rate of 20% after rupture. It is a vascular degenerative condition different from occlusive arterial diseases. The size of the aneurysm is the most important determining factor in its clinical management. However, other measures of the AAA geometry that are currently not used clinically may also influence its rupture risk. With this in mind, the objectives of this work are to develop an algorithm to calculate the AAA wall thickness and abdominal aortic diameter at planes orthogonal to the vessel centerline, and to quantify the effect of geometric indices derived from this algorithm on the overall classification accuracy of AAA based on whether they were electively or emergently repaired. Such quantification was performed based on a retrospective review of existing medical records of 150 AAA patients (75 electively repaired and 75 emergently repaired). Using an algorithm implemented within the MATLAB computing environment, 10 diameter- and wall thickness-related indices had a significant difference in their means when calculated relative to the AAA centerline compared to calculating them relative to the medial axis. Of these 10 indices, nine were wall thickness-related while the remaining one was the maximum diameter (Dmax). Dmax calculated with respect to the medial axis is over-estimated for both electively and emergently repaired AAA compared to its counterpart with respect to the centerline. C5.0 decision trees, a machine learning classification algorithm implemented in the R environment, were used to construct a statistical classifier. The decision trees were built by splitting the data into 70% for training and 30% for testing, and the properties of the classifier were estimated based on 1000 random combinations of the 70/30 data split. The ensuing model had average and maximum classification accuracies of 81.0 and 95.6%, respectively, and revealed that the three most significant indices in classifying AAA are, in order of importance: AAA centerline length, L2-norm of the Gaussian curvature, and AAA wall surface area. Therefore, we infer that the aforementioned three geometric indices could be used in a clinical setting to assess the risk of AAA rupture by means of a decision tree classifier. This work provides support for calculating cross-sectional diameters and wall thicknesses relative to the AAA centerline and using size and surface curvature based indices in classification studies of AAA.

中文翻译：

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基于决策树的腹主动脉瘤的几何量化措施分类。

腹主动脉瘤（AAA）是无症状的主动脉疾病，破裂后存活率为20％。它是一种与闭塞性动脉疾病不同的血管退化性疾病。动脉瘤的大小是其临床管理中最重要的决定因素。但是，目前尚未在临床上使用的AAA几何尺寸的其他措施也可能会影响其破裂风险。考虑到这一点，这项工作的目的是开发一种算法，以计算与血管中心线正交的平面上的AAA壁厚和腹主动脉直径，并量化由此算法得出的几何指标对总体分类准确性的影响AAA的选择，取决于他们是经过选择性维修还是紧急维修。基于对150名AAA患者的现有病历的回顾性回顾（其中75例经选择性修复，而75例紧急修复），进行了定量分析。使用在MATLAB计算环境中实现的算法，与相对于中轴进行计算相比，相对于AAA中心线进行计算时，与直径和壁厚相关的10个指数在均值上存在显着差异。在这10个指标中，有9个与壁厚有关，而其余的是最大直径（Dmax）。相对于中线而言，相对于中间轴计算的Dmax对于选择性和紧急修复的AAA都被高估了。C5.0决策树，一种在R环境中实现的机器学习分类算法，用来构造统计分类器。通过将数据分为70％用于训练和30％用于测试来构建决策树，并基于70/30数据拆分的1000个随机组合来估算分类器的属性。随后的模型分别具有81.0和95.6％的平均分类精度，并且揭示了AAA分类中三个最重要的指标，按照重要性顺序依次为：AAA中线长度，高斯曲率的L2-范数和AAA壁表面积。因此，我们推断可以通过决策树分类器在临床环境中使用上述三个几何指标来评估AAA破裂的风险。