Evaluation of predictive models of aneurysm focal growth and bleb development using machine learning techniques.

ABSTRACT:

Background

The presence of blebs increases the rupture risk of intracranial aneurysms (IAs).

Objective

To evaluate whether cross-sectional bleb formation models can identify aneurysms with focalized enlargement in longitudinal series.

Methods

Hemodynamic, geometric, and anatomical variables derived from computational fluid dynamics models of 2265 IAs from a cross-sectional dataset were used to train machine learning (ML) models for bleb development. ML algorithms, including logistic regression, random forest, bagging method, support vector machine, and K-nearest neighbors, were validated using an independent cross-sectional dataset of 266 IAs. The models' ability to identify aneurysms with focalized enlargement was evaluated using a separate longitudinal dataset of 17

SUBMITTER: Hadad S 

PROVIDER: S-EPMC10674044 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature