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Machine-Aided Detection of SARS-CoV-2 from Complete Blood Count

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Intelligent and Safe Computer Systems in Control and Diagnostics (DPS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 545))

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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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Author information

Authors and Affiliations

  1. Gdańsk University of Technology, Gdańsk, Poland

    Barbara Klaudel, Aleksander Obuchowski & Zdzisław Kowalczuk

  2. Medical University of Gdańsk, Gdańsk, Poland

    Małgorzata Dąbrowska & Kornelia Sałaga-Zaleska

  3. University Clinical Centre in Gdańsk, Gdańsk, Poland

    Małgorzata Dąbrowska & Kornelia Sałaga-Zaleska

Authors
  1. Barbara Klaudel
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  2. Aleksander Obuchowski
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  3. Małgorzata Dąbrowska
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  4. Kornelia Sałaga-Zaleska
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  5. Zdzisław Kowalczuk
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Corresponding author

Correspondence to Barbara Klaudel .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, Poland

    Zdzislaw Kowalczuk

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Klaudel, B., Obuchowski, A., Dąbrowska, M., Sałaga-Zaleska, K., Kowalczuk, Z. (2023). Machine-Aided Detection of SARS-CoV-2 from Complete Blood Count. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_2

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