Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.

ABSTRACT:

Introduction

Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.

Methods

Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps we

SUBMITTER: MacIntosh BJ 

PROVIDER: S-EPMC10568013 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature