Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages

Smorchkova A.K.; Khoruzhaya A.N.; Kremneva E.I.; Petryaikin A.V.

doi:https://doi.org/10.17116/neiro20238702185

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Abstract:

This review discusses pooled experience of creation, implementation and effectiveness of machine learning technologies in CT-based diagnosis of intracranial hemorrhages. The authors analyzed 21 original articles between 2015 and 2022 using the following keywords: «intracranial hemorrhage», «machine learning», «deep learning», «artificial intelligence». The review contains general data on basic concepts of machine learning and also considers in more detail such aspects as technical characteristics of data sets used for creation of AI algorithms for certain type of clinical task, their possible impact on effectiveness and clinical experience.

Keywords:

intracranial hemorrhage

machine learning

artificial intelligence

deep learning

Authors:

Smorchkova A.K.

Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID: 0000-0002-9766-3390

Khoruzhaya A.N.

Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID: 0000-0003-4857-5404

Kremneva E.I.

Research Center of Neurology

Petryaikin A.V.

Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies

ORCID: 0000-0003-1694-4682

Received:

13.12.2022

Accepted:

27.01.2023

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References:

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