In Journal of cardiovascular translational research
Fast-growing abdominal aortic aneurysms (AAA) have a high rupture risk and poor outcomes if not promptly identified and treated. Our primary objective is to improve the differentiation of small AAAs' growth status (fast versus slow-growing) through a combination of patient health information, computational hemodynamics, geometric analysis, and artificial intelligence. 3D computed tomography angiography (CTA) data available for 70 patients diagnosed with AAAs with known growth status were used to conduct geometric and hemodynamic analyses. Differences among ten metrics (out of ninety metrics) were statistically significant discriminators between fast and slow-growing groups. Using a support vector machine (SVM) classifier, the area under receiving operating curve (AUROC) and total accuracy of our best predictive model for differentiation of AAAs' growth status were 0.86 and 77.50%, respectively. In summary, the proposed analytics has the potential to differentiate fast from slow-growing AAAs, helping guide resource allocation for the management of patients with AAAs.
Rezaeitaleshmahalleh Mostafa, Sunderland Kevin W, Lyu Zonghan, Johnson Tonie, King Kristin, Liedl David A, Hofer Janet M, Wang Min, Zhang Xiaoming, Kuczmik Wiktoria, Rasmussen Todd E, McBane Robert D, Jiang Jingfeng
2023-Jan-05
Abdominal aortic aneurysm, Aneurysm Growth, Computational hemodynamics, Machine learning, Predictive modeling
