Optical Method supported by Machine Learning for Urinary Tract Infection Detection and Urosepsis Risk Assessment.

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 culture of clinical E. coli strains. To provide reliable classification of results assistance of 27 algorithms were 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. This article is protected by copyright. All rights reserved.