"Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Vel" by J Jiang, M Rezaeitaleshmahalleh et al.
 

Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience

Document Type

Article

Publication Date

5-9-2023

Abstract

Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively.

Publication Title

Journal of cardiovascular translational research

Link to Full Text

Plum Print visual indicator of research metrics
PlumX Metrics
  • Citations
    • Citation Indexes: 6
  • Usage
    • Abstract Views: 4
  • Captures
    • Readers: 3
  • Mentions
    • News Mentions: 1
see details

Share

COinS