2023
DOI: 10.3389/fneur.2023.1244672
|View full text |Cite
|
Sign up to set email alerts
|

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

Bradley J. MacIntosh,
Qinghui Liu,
Till Schellhorn
et al.

Abstract: IntroductionRadiological 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.MethodsNon-contrast CT imaging data from 1263 pat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
references
References 35 publications
0
0
0
Order By: Relevance