%0 Journal Article
%T Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics.
%A Abu-Aqil G
%A Suleiman M
%A Lapidot I
%A Huleihel M
%A Salman A
%J Spectrochim Acta A Mol Biomol Spectrosc
%V 314
%N 0
%D 2024 Jun 5
%M 38513317
%F 4.831
%R 10.1016/j.saa.2024.124141
%X 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.