The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.

中文翻译：

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机器学习算法对腹部主动脉瘤的比较分类分析。

这项工作的目的是基于腹主动脉瘤的人口统计学，几何学和生物力学属性对图像进行分类。我们回顾性回顾了上次随访后1-6个月内分别接受择期或急诊修复的100例无症状和50例有症状AAA患者的现有人口统计学和腹部CT血管造影图像。在MATLAB计算平台内开发的内部脚本用于分割临床图像，计算AAA几何的53个描述符以及生成适用于有限元分析（FEA）的体积网格。使用第三方FEA解算器，根据壁应力分布计算了四个生物力学标记。八种机器学习算法（MLA）用于根据人口统计学，几何学和生物力学变量的歧视潜力来开发分类模型。通过准确度，接收器工作特性曲线（AUC）下的面积，灵敏度，特异性和预测精度来评估算法的总体分类性能。在将单个AAA归类为无症状的AAA时，发现通用加性模型（GAM）具有最高的准确性（87％），AUC（89％）和灵敏度（78％）和第三高的特异性（92％）。或有症状的。k最近邻分类器产生最高的特异性（96％）。GAM使用了七个标记（六个几何标记和一个生物力学标记）来开发分类器。最大横向尺寸 在分类分析中，最大直径的平均壁厚和空间平均的壁应力是最有影响力的标记。第二个分类分析表明，与使用带有七个几何和生物力学标记的GAM相比，单独使用最大直径会导致较低的准确性（79％）。我们从这些结果中得出结论，生物力学和几何学测量方法本身不足以充分区分无症状和症状性AAA人群样本，而MLA通过结合患者的人口统计学，几何学和生物力学属性，为破裂风险分层提供了一种统计方法。特定的AAA。第二个分类分析表明，与使用带有七个几何和生物力学标记的GAM相比，单独使用最大直径会导致较低的准确性（79％）。我们从这些结果中得出结论，生物力学和几何学测量方法本身不足以充分区分无症状和症状性AAA人群样本，而MLA通过结合患者的人口统计学，几何学和生物力学属性，为破裂风险分层提供了一种统计方法。特定的AAA。第二个分类分析表明，与使用带有七个几何和生物力学标记的GAM相比，单独使用最大直径会导致较低的准确性（79％）。我们从这些结果中得出结论，生物力学和几何学测量方法本身不足以充分区分无症状和症状性AAA人群样本，而MLA通过结合患者的人口统计学，几何学和生物力学属性，为破裂风险分层提供了一种统计方法。特定的AAA。