Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

ABSTRACT:

Objective

The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function.

Methods

This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman co

SUBMITTER: Peng Y 

PROVIDER: S-EPMC10848172 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature