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dc.contributor.author
Weis, Caroline V.
dc.contributor.author
Jutzeler, Catherine R.
dc.contributor.author
Borgwardt, Karsten
dc.date.accessioned
2020-09-24T15:41:10Z
dc.date.available
2020-09-24T06:20:24Z
dc.date.available
2020-09-24T15:41:10Z
dc.date.issued
2020-10
dc.identifier.issn
1470-9465
dc.identifier.issn
1198-743X
dc.identifier.other
10.1016/j.cmi.2020.03.014
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/442204
dc.identifier.doi
10.3929/ethz-b-000442204
dc.description.abstract
Background The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination. Objectives The aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra. Data sources Using PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification. Study eligibility criteria Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded. Methods A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted. Results From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets. Conclusions A growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Antimicrobial resistance
en_US
dc.subject
Antimicrobial susceptibility testing
en_US
dc.subject
Antimicrobial treatment
en_US
dc.subject
Machine learning
en_US
dc.subject
MALDI-TOF MS
en_US
dc.subject
Microbial identification
en_US
dc.title
Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-03-23
ethz.journal.title
Clinical Microbiology and Infection
ethz.journal.volume
26
en_US
ethz.journal.issue
10
en_US
ethz.journal.abbreviated
Clin Microbiol Infect
ethz.pages.start
1310
en_US
ethz.pages.end
1317
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Precision-Medicine for Neurological Disorders: Harnessing the Power of Big Data and Machine Learning for Biomarker Discovery and Drug Repositioning Strategies
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.grant.agreementno
186101
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Ambizione
ethz.date.deposited
2020-09-24T06:20:31Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-24T15:41:21Z
ethz.rosetta.lastUpdated
2020-09-24T15:41:21Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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