A simple and effective machine learning model for predicting the stability of intracranial aneurysms using CT angiography.

ABSTRACT:

Background

It is vital to accurately and promptly distinguish unstable from stable intracranial aneurysms (IAs) to facilitate treatment optimization and avoid unnecessary treatment. The aim of this study is to develop a simple and effective predictive model for the clinical evaluation of the stability of IAs.

Methods

In total, 1,053 patients with 1,239 IAs were randomly divided the dataset into training (70%) and internal validation (30%) datasets. One hundred and ninety seven patients with 229 IAs from another hospital were evaluated as an external validation dataset. The prediction models were developed using machine learning based on clinical information, manual parameters, and radiomic features. In addition, a simple model for predicting the stability of IAs was develope

SUBMITTER: Luo S 

PROVIDER: S-EPMC11219573 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature