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Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures.

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Developing new algorithms to measure abdominal aortic aneurysm (AAA) wall thickness and diameter can improve rupture risk assessment. Geometric indices, including centerline length and curvature, significantly aid in classifying AAA repair urgency.

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Area of Science:

  • Vascular Surgery
  • Medical Imaging Analysis
  • Computational Anatomy

Background:

  • Abdominal aortic aneurysm (AAA) is a dangerous vascular condition with low survival rates post-rupture.
  • Current clinical management primarily relies on aneurysm size, but other geometric factors may influence rupture risk.
  • Accurate assessment of AAA geometry is crucial for predicting rupture and guiding treatment decisions.

Purpose of the Study:

  • To develop an algorithm for calculating AAA wall thickness and diameter orthogonal to the vessel centerline.
  • To quantify the impact of novel geometric indices on classifying AAA repair urgency (elective vs. emergent).
  • To assess the clinical utility of these indices in predicting AAA rupture risk.

Main Methods:

  • A retrospective review of 150 AAA patient records (75 elective, 75 emergent repairs).
  • Development of a MATLAB algorithm to compute diameter and wall thickness relative to the AAA centerline.
  • Utilized C5.0 decision trees in R for classification, with a 70/30 training/testing data split and 1000 iterations.

Main Results:

  • Nine wall thickness indices and maximum diameter (Dmax) showed significant differences when measured relative to the centerline versus the medial axis.
  • Centerline-based Dmax was overestimated compared to medial axis measurements for both repair types.
  • The classification model achieved average and maximum accuracies of 81.0% and 95.6%, respectively.
  • Key predictors for AAA classification were AAA centerline length, L2-norm of Gaussian curvature, and AAA wall surface area.

Conclusions:

  • Geometric indices derived from centerline-based measurements, particularly centerline length, Gaussian curvature, and wall surface area, are significant for AAA classification.
  • These findings support the use of centerline-based calculations and advanced geometric indices for improved AAA rupture risk assessment.
  • The developed decision tree model demonstrates potential for clinical application in stratifying AAA patient risk.