Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

ABSTRACT:

Objectives

We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods.

Methods

ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operatin

SUBMITTER: Jiang L 

PROVIDER: S-EPMC11399198 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature