Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.

ABSTRACT:

Introduction

We developed and externally validated a fully automated algorithm using deep learning to detect large vessel occlusion (LVO) in computed tomography angiography (CTA).

Method

A total of 2,045 patients with acute ischemic stroke who underwent CTA were included in the development of our model. We validated the algorithm using two separate external datasets: one with 64 patients (external 1) and another with 313 patients (external 2), with ischemic stroke. In the context of current clinical practice, thrombectomy amenable vessel occlusion (TAVO) was defined as an occlusion in the intracranial internal carotid artery (ICA), or in the M1 or M2 segment of the middle cerebral artery (MCA). We employed the U-Net for vessel segmentation on the maximum intensity projection

SUBMITTER: Han JH 

PROVIDER: S-EPMC11306064 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature