Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa's susceptibility to antibiotics

Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of ∼20 min after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82–90%.

Original languageEnglish
Article number121080
JournalSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
Volume274
DOIs
StatePublished - 5 Jun 2022

Keywords

  • Antibiotic resistance
  • Antibiotics
  • Bacterial susceptibility to antibiotics
  • Infra-red microscopy
  • Machine learning
  • Pseudomonas aeruginosa

ASJC Scopus subject areas

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

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