C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.

Journal: Scientific reports
PMID:

Abstract

The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation of various origins, oncological, cardiovascular, bacterial or viral events. In this study, we describe an interferometric sensor able to detect the CRP level for distinguishing between no-inflammation and inflammation states. The measurement head was made of a single mode optical fiber with a microsphere structure created at the tip. Its surface has been biofunctionalized for specific CRP bonding. Standardized CRP solutions were measured in the range of 1.9 µg/L to 333 mg/L and classified in the initial phase of the study. The real samples obtained from hospitalized patients with diagnosed Urinary Tract Infection or Urosepsis were then investigated. 27 machine learning classifiers were tested for labeling the phantom samples as normal or high CRP levels. With the use of the ExtraTreesClassifier we obtained an accuracy of 95% for the validation dataset. The results of real samples classification showed up to 100% accuracy for the validation dataset using XGB classifier.

Authors

  • Kacper Cierpiak
    Department of Metrology and Optoelectronics, Faculty of Informatics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233, Gdańsk, Poland.
  • Paweł Wityk
    Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland.
  • Monika Kosowska
    Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Al. prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland.
  • Patryk Sokołowski
    Department of Metrology and Optoelectronics, Faculty of Informatics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233, Gdańsk, Poland.
  • Tomasz Talaśka
    Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Al. prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland.
  • Jakub Gierowski
    Kayon sp. z o.o., Romualda Traugutta 115c, 80-226, Gdańsk, Poland.
  • Michał J Markuszewski
    Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland.
  • Małgorzata Szczerska
    Department of Metrology and Optoelectronics, Faculty of Informatics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233, Gdańsk, Poland. malszcze@pg.edu.pl.