Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study

Mostafa Rezaeitaleshmahalleh, Kevin W. Sunderland, Zonghan Lyu, Tonie Johnson, Kristin King, David A. Liedl, Janet M. Hofer, Min Wang, Xiaoming Zhang, Wiktoria Kuczmik, Todd E. Rasmussen, Robert D. McBane, Jingfeng Jiang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)874-885
Number of pages12
JournalJournal of Cardiovascular Translational Research
Volume16
Issue number4
DOIs
StatePublished - Aug 2023
Externally publishedYes

Keywords

  • Abdominal aortic aneurysm
  • Aneurysm Growth
  • Computational hemodynamics
  • Machine learning
  • Predictive modeling

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