A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.

ABSTRACT:

Background

Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-resonance-imaging commonly fail to detect hyperacute POCI. Studies suggest expert inspection of Computed Tomography Perfusion (CTP) improves diagnosis of POCI. In many settings, there is limited access to specialist expertise. Deep-learning has been successfully applied to automate imaging interpretation. This study aimed to develop and validate a deep-learning approach for the classification of POCI using CTP.

Methods

Data were analysed from 3541-patients from the International-stroke-perfusion-registry (INSPIRE). All patients with baseline multimodal-CT and follow-up imaging performed at 24-48 h were identified. A cohort of 541-pat

SUBMITTER: Edwards LS 

PROVIDER: S-EPMC11786091 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature