InceptionV4 and SEResNet101: precise predictors of intracranial hemorrhage and collateral circulation post-ischemic stroke intervention.

ABSTRACT:

Background

Ischemic stroke (IS) is a major global health issue. The risk of intracranial hemorrhage (ICH) after interventional treatment and the status of collateral circulation significantly affect patient prognosis. Traditional diagnostic methods for predicting ICH and collateral circulation are limited. This study aimed to develop a more accurate prediction method using deep learning (DL) models.

Methods

A meta-analysis was conducted on relevant literature. Five DL models (DenseNet169, InceptionResNetV2, InceptionV4, MobileNetV3Small, and SEResNet101) were trained and tested with preoperative CT images from 58 patients and the CQ500 dataset. An MCAO mouse model was established to identify biomarkers.

Results

AI showed high accuracy in predicting ICH from CT images.

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12516262 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature