Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model.

ABSTRACT: Rationale: Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early invisible AIS in NCCT and evaluate its diagnostic performance and capacity for assisting radiologists in decision making. Methods: In this multi-center, multi-manufacturer retrospective study, 1136 patients with suspected AIS but invisible lesions in NCCT were collected from two geographically distant institutions between May 2012 to May 2021. The AIS lesions were confirmed based on the follow-up diffusion-weighted imaging and clinical diagnosis. The deep-learning model was comprised of two deep convolutional neural netw

SUBMITTER: Lu J 

PROVIDER: S-EPMC9330528 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature