Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans.

ABSTRACT:

Background

To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology (aneurysms, hypertensive hemorrhage, arteriovenous malformation (AVM), Moyamoya disease (MMD), cavernous malformation (CM), or other causes) based on non-contrast computed tomography (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting.

Methods

The deep learning model was developed with 1,868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians' performance. We further designed a simulated study to compa

SUBMITTER: Zhao M 

PROVIDER: S-EPMC11871901 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature