A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features.
A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features.
复制标题
使用几何、生物力学和患者特定的历史临床特征来评估腹主动脉瘤严重程度的机器学习方法。
DOI：
10.1117/12.2549277
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Linte,CristianA
中科院分区：
文献类型：
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作者：
Jalalahmadi,Golnaz;Helguera,María;Linte,CristianA
最近监测腹主动脉瘤（AAA）严重程度的研究表明，仅依赖最大横向直径（Dmax）可能不足以预测AAA破裂风险。此外，几何指数、生物力学参数、材料特性和患者特定历史数据影响AAA形态，表明需要一种综合方法，将所有因素结合起来，以更准确地估计AAA严重程度。我们使用从66名患者中提取的45个特征实现了一种机器学习算法。该模型是使用J48决策树算法生成的，目的是最大限度地提高模型的准确性。使用三个不同的特征集来评估预测率：i）使用Dmax作为单个特征集，ii）使用所有特征的集合，以及最后iii）使用通过BestFirst特征选择算法选择的特征集。我们的研究结果表明，BestFirst特征选择产生了最高的预测精度。这些结果表明，综合捕获AAA行为的几个特定参数的组合可以实现AAA严重程度的适当评估，表明机器学习对该应用的潜在益处。
Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter (Dmax) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using Dmax as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.