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.
The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patientspecific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.
KEYWORDS: Magnetic resonance imaging, Tissues, Process modeling, Data modeling, Finite element methods, 3D modeling, Image segmentation, 3D image processing, Arteries, Computed tomography
Abdominal aortic aneurysm (AAA) is known as a leading cause of death in the United States. AAA is an abnormal dilation of the aorta, which usually occurs below the renal arteries and causes an expansion at least 1.5 times its normal diameter. It has been shown that biomechanical parameters of the aortic tissue coupled with a set of specific geometric parameters characterizing the vessel expansion, affect the risk of aneurysm rupture. Here, we developed a numerical framework that incorporates both biomechanical and geometrical factors to study the behavior of abdominal aortic aneurysm. Our workflow enables the extraction of the aneurysm geometry from both clinical quality, as well as low-resolution MR images. We used a two-parameter, hyper-elastic, isotropic, incompressible material to model the vessel tissue. Our numerical model was tested using both synthetic and mouse data and we evaluated the effects of the geometrical and biomechanical properties on the developed peak wall stress. In addition, we performed several parameter sensitivity studies to investigate the effect of different factors affecting the AAA and its behavior and rupture. Lastly, relationships between different geometrical and biomechanical parameters and peak wall stress were determined. These studies help us better understand vessel tissue response to various loading, geometry and biomechanics conditions, and we plan to further correlate these findings with the pathophysiological conditions from a patient population diagnosed with abdominal aortic aneurysms.
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