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Smorchkova A.K.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Khoruzhaya A.N.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Kremneva E.I.
Research Center of Neurology
Petryaikin A.V.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages
Journal: Burdenko's Journal of Neurosurgery. 2023;87(2): 85‑91
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To cite this article:
Smorchkova AK, Khoruzhaya AN, Kremneva EI, Petryaikin AV. Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages. Burdenko's Journal of Neurosurgery.
2023;87(2):85‑91. (In Russ., In Engl.)
https://doi.org/10.17116/neiro20238702185
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.
Authors:
Smorchkova A.K.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Khoruzhaya A.N.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Kremneva E.I.
Research Center of Neurology
Petryaikin A.V.
Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies
Received:
13.12.2022
Accepted:
27.01.2023
List of references:
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