Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics

George Abu-Aqil, Manal Suleiman, Itshak Lapidot, Mahmoud Huleihel, Ahmad Salman

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.

Original languageEnglish
Article number124141
JournalSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
Volume314
DOIs
StatePublished - 5 Jun 2024

Keywords

  • Bacterial resistance to antibiotics
  • Infrared spectroscopy
  • Klebsiella pneumoniae
  • Machine learning
  • Urinary tract infection (UTI)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Spectroscopy

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