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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
Abstract
The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain up to 97% accuracy of the measurement method with the use of use of machine learning. The method was validated on urine samples from 241 patients. The advantages of the proposed solution are the simplicity of the sensor, mobility, versatility, and low cost of the test.
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Authors (6)
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Michał Markuszewski
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1002/jbio.202300095
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Journal of Biophotonics
no. 16,
pages 1 - 8,
ISSN: 1864-063X - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Wityk P., Sokołowski P., Szczerska M., Cierpiak K., Krawczyk B., Markuszewski M.: Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment// Journal of Biophotonics -Vol. 16,iss. 9 (2023), s.1-8
- DOI:
- Digital Object Identifier (open in new tab) 10.1002/jbio.202300095
- Sources of funding:
-
- Inkubator GUMed
- Verified by:
- Gdańsk University of Technology
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