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

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

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, lik...

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Bibliographic Details
Main Authors: Diana Zarei, Mahbod Issaiy, Shahriar Kolahi, David S. Liebeskind
Format: Article
Language:English
Published: Wiley 2024-07-01
Series:Stroke: Vascular and Interventional Neurology
Subjects:
computed tomography
deep learning
intracerebral hemorrhage
meta‐analysis
systematic review
Online Access:https://www.ahajournals.org/doi/10.1161/SVIN.123.001314
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https://www.ahajournals.org/doi/10.1161/SVIN.123.001314

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