Computational methods have played an important role in health care in recent years, as determining parameters that affect a certain medical condition is not possible in experimental conditions in many cases. Computational fluid dynamics (CFD) methods have been used to accurately determine the nature of blood flow in the cardiovascular and nervous systems and air flow in the respiratory system, thereby giving the surgeon a diagnostic tool to plan treatment accordingly. Machine learning or data mining (MLD) methods are currently used to develop models that learn from retrospective data to make a prediction regarding factors affecting the progression of a disease. These models have also been successful in incorporating factors such as patient history and occupation. MLD models can be used as a predictive tool to determine rupture potential in patients with abdominal aortic aneurysms (AAA) along with CFD-based prediction of parameters like wall shear stress and pressure distributions. A combination of these computer methods can be pivotal in bridging the gap between translational and outcomes research in medicine. This paper reviews the use of computational methods in the diagnosis and treatment of AAA.

Abdominal Aortic Aneurysms (AAA) occur because of dilation of the infra renal aorta to more than 150% of its initial diameter. Progression to rupture is aided by several pathophysiological and biomechanical factors. Surgical intervention is recommended when the aneurysm maximum transverse diameter (DAAA) exceeds 55 mm. A system model that incorporates biomechanical parameters will improve prognosis and establish a relationship between AAA geometry and rupture risk. Two Asian patient specific AAA geometries were obtained from an IRB approved vascular database. A biomechanical model based on the fluid-structure interaction (FSI) method was developed for a small aneurysm with DAAA of 35 mm and a large aneurysm with a corresponding diameter of 75 mm. The small aneurysm (patient 1) developed a maximum principal stress (PS1) of 3.16e5 Pa and the large aneurysm (patient 2) developed a PS1 of 2.32e5 Pa. Maximum deformation of arterial wall was 0.0020 m and 0.0022 m for patients 1 and 2 respectively. Location of maximum integral wall shear stress (WSS) (fluid) was different from that of PS1. Induced WSS was also higher in patient 1 (18.74 Pa vs 12.88 Pa). An FSI model incorporating the effect of both the structural and fluid domains aids in better understanding of the mechanics of AAA rupture. Patient 1 having a lower DAAA than patient 2, developed a larger PS1 and WSS. It may be concluded that DAAA may not be the sole determinant of AAA rupture risk.

This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter.

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