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Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform

Author

Listed:
  • Alexander Sturm

    (Resistell AG, Hofackerstrasse 40)

  • Grzegorz Jóźwiak

    (Resistell AG, Hofackerstrasse 40)

  • Marta Pla Verge

    (Resistell AG, Hofackerstrasse 40)

  • Laura Munch

    (Resistell AG, Hofackerstrasse 40)

  • Gino Cathomen

    (Resistell AG, Hofackerstrasse 40)

  • Anthony Vocat

    (Resistell AG, Hofackerstrasse 40)

  • Amanda Luraschi-Eggemann

    (Resistell AG, Hofackerstrasse 40)

  • Clara Orlando

    (Resistell AG, Hofackerstrasse 40)

  • Katja Fromm

    (Resistell AG, Hofackerstrasse 40)

  • Eric Delarze

    (Resistell AG, Hofackerstrasse 40)

  • Michał Świątkowski

    (Resistell AG, Hofackerstrasse 40)

  • Grzegorz Wielgoszewski

    (Resistell AG, Hofackerstrasse 40)

  • Roxana M. Totu

    (Resistell AG, Hofackerstrasse 40)

  • María García-Castillo

    (Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Carretera de Colmenar Km 9,1)

  • Alexandre Delfino

    (Lausanne University Hospital (CHUV) & University of Lausanne (UNIL))

  • Florian Tagini

    (Lausanne University Hospital (CHUV) & University of Lausanne (UNIL))

  • Sandor Kasas

    (École Polytechnique Fédérale de Lausanne (EPFL) and University of Lausanne (UNIL)
    Centre Universitaire Romand de Médecine Légale (UFAM) & Université de Lausanne (UNIL))

  • Cornelia Lass-Flörl

    (Medizinische Universität Innsbruck, Schöpfstraße 41)

  • Ronald Gstir

    (Medizinische Universität Innsbruck, Schöpfstraße 41)

  • Rafael Cantón

    (Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Carretera de Colmenar Km 9,1
    CIBER de Enfermedades Infecciosas (CIBERINFEC). Instituto de Salud Carlos III. Sinesio Delgado 4)

  • Gilbert Greub

    (Lausanne University Hospital (CHUV) & University of Lausanne (UNIL))

  • Danuta Cichocka

    (Resistell AG, Hofackerstrasse 40)

Abstract

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.

Suggested Citation

  • Alexander Sturm & Grzegorz Jóźwiak & Marta Pla Verge & Laura Munch & Gino Cathomen & Anthony Vocat & Amanda Luraschi-Eggemann & Clara Orlando & Katja Fromm & Eric Delarze & Michał Świątkowski & Grzego, 2024. "Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46213-y
    DOI: 10.1038/s41467-024-46213-y
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    References listed on IDEAS

    as
    1. Vinodh Kandavalli & Praneeth Karempudi & Jimmy Larsson & Johan Elf, 2022. "Rapid antibiotic susceptibility testing and species identification for mixed samples," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Ofer Fridman & Amir Goldberg & Irine Ronin & Noam Shoresh & Nathalie Q. Balaban, 2014. "Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations," Nature, Nature, vol. 513(7518), pages 418-421, September.
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