Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.

ABSTRACT:

Background

Stroke, a major global health issue, is broadly categorized into ischemic and hemorrhagic types. The volume of hemorrhage on noncontrast computed tomography guides the treatment options and hints at prognosis. Conventional approaches to calculate intracerebral hemorrhage (ICH) volume, like the ABC/2 method, typically rely on an assumed standard shape and might be inaccurate. Advances in deep learning have significantly improved noncontrast computed tomography's capabilities in ICH volume estimation. This study conducts a comprehensive systematic review and meta-analysis to evaluate the precision of deep learning algorithms in delineating ICH on noncontrast computed tomography.

Methods

A systematic review and meta-analysis, adhering to Preferred Reporting Items for

SUBMITTER: Zarei D 

PROVIDER: S-EPMC12778524 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature