The Evaluation of Machine Learning Models Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI–TOF–MS) Spectra for the Prediction of Antibiotic Resistance in Klebsiella pneumoniae
Authors: Fordham, S.M.E.
Journal: Microbiologyopen
Publication Date: 01/04/2026
Volume: 15
Issue: 2
eISSN: 2045-8827
DOI: 10.1002/mbo3.70257
Abstract:Antimicrobial resistance in Klebsiella pneumoniae poses a major clinical challenge, driving development in rapid, diagnostic strategies that extend beyond conventional susceptibility testing. Twenty-three studies demonstrate that using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF–MS) spectra to create machine learning (ML) models yields rapid and accurate predictions of antibiotic resistance in K. pneumoniae. Across these studies, most models focused on carbapenem resistance and achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values consistently above 0.90, with ensemble algorithms, particularly Random Forest, XGBoost, and Light Gradient Boosting Machine, and deep learning models such as Convolutional Neural Networks attaining accuracies as high as 97% and even AUROCs reaching 0.99 or higher. Sample sizes ranged from 35 to over 15,000 isolates, reinforcing the robustness of these findings across diverse clinical settings. In addition to high discrimination performance, this evaluation reports that ML models developed using MALDI–TOF–MS spectra shorten diagnostic turnaround from days (48–96 h with conventional methods) to minutes or hours, using existing MALDI–TOF–MS equipment for economical implementation. However, ML diagnostic tools remain constrained by limited external validation, spectra preprocessing protocols, and variability between different MALDI–TOF–MS platforms. These limitations may restrict model generalizability and clinical translation, highlighting the need for standardized workflows and larger multicenter evaluations.
https://eprints.bournemouth.ac.uk/41835/
Source: Scopus
The Evaluation of Machine Learning Models Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) Spectra for the Prediction of Antibiotic Resistance in Klebsiella pneumoniae.
Authors: Fordham, S.M.E.
Journal: Microbiologyopen
Publication Date: 04/2026
Volume: 15
Issue: 2
Pages: e70257
eISSN: 2045-8827
DOI: 10.1002/mbo3.70257
Abstract:Antimicrobial resistance in Klebsiella pneumoniae poses a major clinical challenge, driving development in rapid, diagnostic strategies that extend beyond conventional susceptibility testing. Twenty-three studies demonstrate that using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) spectra to create machine learning (ML) models yields rapid and accurate predictions of antibiotic resistance in K. pneumoniae. Across these studies, most models focused on carbapenem resistance and achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values consistently above 0.90, with ensemble algorithms, particularly Random Forest, XGBoost, and Light Gradient Boosting Machine, and deep learning models such as Convolutional Neural Networks attaining accuracies as high as 97% and even AUROCs reaching 0.99 or higher. Sample sizes ranged from 35 to over 15,000 isolates, reinforcing the robustness of these findings across diverse clinical settings. In addition to high discrimination performance, this evaluation reports that ML models developed using MALDI-TOF-MS spectra shorten diagnostic turnaround from days (48-96 h with conventional methods) to minutes or hours, using existing MALDI-TOF-MS equipment for economical implementation. However, ML diagnostic tools remain constrained by limited external validation, spectra preprocessing protocols, and variability between different MALDI-TOF-MS platforms. These limitations may restrict model generalizability and clinical translation, highlighting the need for standardized workflows and larger multicenter evaluations.
https://eprints.bournemouth.ac.uk/41835/
Source: PubMed
The Evaluation of Machine Learning Models Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) Spectra for the Prediction of Antibiotic Resistance in <i>Klebsiella pneumoniae</i>
Authors: Fordham, S.M.E.
Journal: MICROBIOLOGYOPEN
Publication Date: 02/03/2026
Volume: 15
Issue: 2
ISSN: 2045-8827
DOI: 10.1002/mbo3.70257
https://eprints.bournemouth.ac.uk/41835/
Source: Web of Science
The Evaluation of Machine Learning Models Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI–TOF–MS) Spectra for the Prediction of Antibiotic Resistance in Klebsiella pneumoniae
Authors: Fordham, S.M.E.
Journal: MicrobiologyOpen
Publication Date: 02/03/2026
Volume: 15
Issue: 2
Publisher: Wiley
eISSN: 2045-8827
ISSN: 2045-8827
Abstract:Antimicrobial resistance in Klebsiella pneumoniae poses a major clinical challenge, driving development in rapid, diagnostic strategies that extend beyond conventional susceptibility testing. Twenty-three studies demonstrate that using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF–MS) spectra to create machine learning (ML) models yields rapid and accurate predictions of antibiotic resistance in K. pneumoniae. Across these studies, most models focused on carbapenem resistance and achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values consistently above 0.90, with ensemble algorithms, particularly Random Forest, XGBoost, and Light Gradient Boosting Machine, and deep learning models such as Convolutional Neural Networks attaining accuracies as high as 97% and even AUROCs reaching 0.99 or higher. Sample sizes ranged from 35 to over 15,000 isolates, reinforcing the robustness of these findings across diverse clinical settings. In addition to high discrimination performance, this evaluation reports that ML models developed using MALDI–TOF–MS spectra shorten diagnostic turnaround from days (48–96 h with conventional methods) to minutes or hours, using existing MALDI–TOF–MS equipment for economical implementation. However, ML diagnostic tools remain constrained by limited external validation, spectra preprocessing protocols, and variability between different MALDI–TOF–MS platforms. These limitations may restrict model generalizability and clinical translation, highlighting the need for standardized workflows and larger multicenter evaluations.
https://eprints.bournemouth.ac.uk/41835/
https://onlinelibrary.wiley.com/doi/full/10.1002/mbo3.70257
Source: Manual
The Evaluation of Machine Learning Models Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) Spectra for the Prediction of Antibiotic Resistance in Klebsiella pneumoniae.
Authors: Fordham, S.M.E.
Journal: MicrobiologyOpen
Publication Date: 04/2026
Volume: 15
Issue: 2
Pages: e70257
eISSN: 2045-8827
ISSN: 2045-8827
DOI: 10.1002/mbo3.70257
Abstract:Antimicrobial resistance in Klebsiella pneumoniae poses a major clinical challenge, driving development in rapid, diagnostic strategies that extend beyond conventional susceptibility testing. Twenty-three studies demonstrate that using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) spectra to create machine learning (ML) models yields rapid and accurate predictions of antibiotic resistance in K. pneumoniae. Across these studies, most models focused on carbapenem resistance and achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values consistently above 0.90, with ensemble algorithms, particularly Random Forest, XGBoost, and Light Gradient Boosting Machine, and deep learning models such as Convolutional Neural Networks attaining accuracies as high as 97% and even AUROCs reaching 0.99 or higher. Sample sizes ranged from 35 to over 15,000 isolates, reinforcing the robustness of these findings across diverse clinical settings. In addition to high discrimination performance, this evaluation reports that ML models developed using MALDI-TOF-MS spectra shorten diagnostic turnaround from days (48-96 h with conventional methods) to minutes or hours, using existing MALDI-TOF-MS equipment for economical implementation. However, ML diagnostic tools remain constrained by limited external validation, spectra preprocessing protocols, and variability between different MALDI-TOF-MS platforms. These limitations may restrict model generalizability and clinical translation, highlighting the need for standardized workflows and larger multicenter evaluations.
https://eprints.bournemouth.ac.uk/41835/
Source: Europe PubMed Central