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Title: Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review.
Author: Sudarshan, V.
Raghavendra, U.
Gudigar, A.
Ciaccio, E.
Vijayananthan, A.
Sahathevan, Ramesh
Acharya, U.
Issue Date: 2022
Publication Title: Biocybernetics and Biomedical Engineering
Volume: 42
Issue: 3
Start Page: 888
End Page: 901
Abstract: The management of intracerebral hemorrhage (ICH) requires prompt diagnostic assessment and recognition. Accurate localization and categorization of ICH-type is crucial. There are two main categories of ICH: 1) hemorrhagic stroke (HS), which occurs in the deeper or subcortical regions of the brain, where the arterial network tapers to fine end-arteries, and, 2) cerebral amyloid angiopathy hemorrhage (CAAH), which occurs at the superficial or cortical-subcortical region of the grey and white matter junction. Computed tomography (CT) and magnetic resonance imaging (MRI) are the most used imaging tools in diagnosing ICH. However, availability, time, and cost often prevent emergent MRI use. Therefore, CT remains the primary tool in the diagnosis of ICH. The assessment of imaging studies is time-dependent, and a radiologist should do a detailed diagnostic evaluation. Human error can occur in a pressured clinical setting, even for highly trained medical professionals. Assisted or automated computer-aided analysis of CT/MRI may help to reduce the assessment time, improve the diagnostic accuracy, better differentiate between types of ICH, and reduce the risk of human errors. This review evaluates CT and MRI's role in distinguishing between the two varieties of ICH-HS and CAAH. It focuses on how CT could be utilized as the preferred diagnostic tool. In addition, we discuss the role of automation using machine learning (ML) and the role or advantages of ML in the automated assessment of CT for the detection and classification of HS and CAAH. We have included our observations for future research and the requirements for further evaluation.
Internal ID Number: 01942
Type: Journal Article
Appears in Collections:Research Output

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