Publications by authors named "A N Khoruzhaya"

Article Synopsis
  • The study aimed to develop and evaluate an ensemble of machine learning models against the pre-trained BERT model for binary classification of intracranial hemorrhage signs in brain CT reports.
  • Data came from 3980 brain CT reports at various medical facilities, using algorithms like CatBoost and logistic regression, and text processing methods including bag of words and TF-IDF.
  • Results showed that the ensemble of selected algorithms achieved high sensitivity (0.93) and specificity (0.90), outperforming the BERT model in classifying medical text data.
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Rationale And Objectives: Post-COVID condition (PCC) is associated with long-term neuropsychiatric symptoms. Magnetic resonance imaging (MRI) in PCC examines the brain metabolism, connectivity, and morphometry. Such techniques are not easily available in routine practice.

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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.

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During the pandemic of novel coronavirus infection (COVID-19), computed tomography (CT) showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia.

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