Surface curvature as a classifier of abdominal aortic aneurysms: A comparative analysis

Kibaek Lee, Junjun Zhu, Judy Shum, Yongjie Zhang, Satish C. Muluk, Ankur Chandra, Mark K. Eskandari, Ender A. Finol*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups.

Original languageEnglish (US)
Pages (from-to)562-576
Number of pages15
JournalAnnals of Biomedical Engineering
Volume41
Issue number3
DOIs
StatePublished - Mar 2013

Keywords

  • Abdominal aortic aneurysm
  • Finite element method
  • Geometry quantification
  • Machine learning
  • Reconstruction
  • Rupture risk
  • Surface curvature

ASJC Scopus subject areas

  • Biomedical Engineering

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