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Abstract
The current gold standard for SARS-CoV-2 detection methods lacks the functionality to perform population screening. Complete blood count (CBC) tests are a cost-effective way to reach a wide range of people – e.g. according to the data of the Central Statistical Office of Poland from 2016, there are 3,000 blood diagnostic laboratories in Poland, and 46% of Polish people have at least one CBC test per year. In our work, we show the possibility of machine detection of SARS-CoV-2 virus on the basis of routine blood tests. The role of the model is to facilitate the screening of SARS-CoV-2 in asymptomatic patients or in the incubation phase. Early research suggests that asymptomatic patients with COVID-19 may develop complications of COVID-19 (e.g., a type of lung injury). The solution we propose has an F1 score of 87.37%. We show the difference in the results obtained on Polish and Italian data sets, challenges in cross-country knowledge transfer and the selection of machine learning algorithms. We also show that CBC-based models can be a convenient, cost-effective and accurate method for the detection of SARS-CoV-2, however, such a model requires validation on an external cohort before being put into clinical practice.
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Authors (5)
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Małgorzata Dąbrowska
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Kornelia Sałaga-zaleska
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Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Klaudel B., Obuchowski A., Dąbrowska M., Sałaga-Zaleska K., Kowalczuk Z.: Machine-aided detection of SARS-CoV-2 from complete blood count// Intelligent and Safe Computer Systems in Control and Diagnostics/ : , 2023, s.17-28
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-16159-9_2
- Verified by:
- Gdańsk University of Technology
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