Franco Pamela, Sotelo Julio, Guala Andrea, Dux-Santoy Lydia, Evangelista Arturo, Rodríguez-Palomares José, Mery Domingo, Salas Rodrigo, Uribe Sergio
Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Electrical Engineering Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Cardio, MR, Chile.
Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Cardio, MR, Chile; School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile.
Comput Biol Med. 2022 Feb;141:105147. doi: 10.1016/j.compbiomed.2021.105147. Epub 2021 Dec 16.
Recent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic parameters from 4D Flow MRI data and find which parameters allow an accurate classification between healthy volunteers (HV) and BAV patients with dilated and non-dilated ascending aorta using machine learning. Sixteen hemodynamic parameters were calculated in the ascending aorta (AAo) and aortic arch (AArch) at peak systole from 4D Flow MRI. We used sequential forward selection (SFS) and principal component analysis (PCA) as feature selection algorithms. Then, eleven machine-learning classifiers were implemented to separate HV and BAV patients (non- and dilated ascending aorta). Multiple correlation patterns from hemodynamic parameters were extracted using hierarchical clustering. The linear discriminant analysis and random forest are the best performing classifiers, using five hemodynamic parameters selected with SFS (velocity angle, forward velocity, vorticity, and backward velocity in AAo; and helicity density in AArch) a 96.31 ± 1.76% and 96.00 ± 0.83% accuracy, respectively. Hierarchical clustering revealed three groups of correlated features. According to this analysis, we observed that features selected by SFS have a better performance than those selected by PCA because the five selected parameters were distributed according to 3 different clusters. Based on the proposed method, we concluded that the feature selection method found five potentially hemodynamic biomarkers related to this disease.
医学成像的最新进展已证实二叶式主动脉瓣（BAV）患者存在血流动力学改变。因此，需要新的血流动力学生物标志物来优化疾病监测并改善患者风险分层。本研究旨在从4D流磁共振成像（MRI）数据中分析和提取血流动力学参数的多种相关模式，并使用机器学习找出哪些参数能够准确区分健康志愿者（HV）与升主动脉扩张和未扩张的BAV患者。在收缩期峰值时，从4D流MRI计算升主动脉（AAo）和主动脉弓（AArch）中的16个血流动力学参数。我们使用顺序向前选择（SFS）和主成分分析（PCA）作为特征选择算法。然后，实施11种机器学习分类器以区分HV和BAV患者（升主动脉未扩张和扩张）。使用层次聚类提取血流动力学参数的多种相关模式。线性判别分析和随机森林是性能最佳的分类器，分别使用SFS选择的五个血流动力学参数（AAo中的速度角、前向速度、涡度和后向速度；以及AArch中的螺旋度密度），准确率分别为96.31±1.76%和96.00±0.83%。层次聚类揭示了三组相关特征。根据该分析，我们观察到SFS选择的特征比PCA选择的特征性能更好，因为所选的五个参数根据3个不同的聚类分布。基于所提出的方法，我们得出结论，特征选择方法发现了五个与该疾病相关的潜在血流动力学生物标志物。
