TY - JOUR
T1 - Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics
AU - Abu-Aqil, George
AU - Suleiman, Manal
AU - Lapidot, Itshak
AU - Huleihel, Mahmoud
AU - Salman, Ahmad
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/6/5
Y1 - 2024/6/5
N2 - 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.
AB - 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.
KW - Bacterial resistance to antibiotics
KW - Infrared spectroscopy
KW - Klebsiella pneumoniae
KW - Machine learning
KW - Urinary tract infection (UTI)
UR - http://www.scopus.com/inward/record.url?scp=85188469385&partnerID=8YFLogxK
U2 - 10.1016/j.saa.2024.124141
DO - 10.1016/j.saa.2024.124141
M3 - Article
C2 - 38513317
AN - SCOPUS:85188469385
SN - 1386-1425
VL - 314
JO - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
JF - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
M1 - 124141
ER -