Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.

ABSTRACT:

Objective

This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).

Methods

Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' read

SUBMITTER: Chen W 

PROVIDER: S-EPMC9723089 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature