Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
18 Mar 2024
18 Mar 2024
Historique:
received:
01
06
2023
accepted:
16
02
2024
medline:
19
3
2024
pubmed:
19
3
2024
entrez:
19
3
2024
Statut:
epublish
Résumé
Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.
Identifiants
pubmed: 38499536
doi: 10.1038/s41467-024-46213-y
pii: 10.1038/s41467-024-46213-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2037Informations de copyright
© 2024. The Author(s).
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