Accurate and Rapid Antibiotic Susceptibility Testing Using a Machine Learning-assisted Nanomotion Technology Platform

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Mar 19

PMID 38499536

Authors

Alexander Sturm

Grzegorz Jozwiak

Marta Pla Verge

Laura Munch

Gino Cathomen

Anthony Vocat

Amanda Luraschi-Eggemann

Clara Orlando

Katja Fromm

Eric Delarze

Michal Swiatkowski

Grzegorz Wielgoszewski

Roxana M Totu

Maria Garcia-Castillo

Alexandre Delfino

Florian Tagini

Sandor Kasas

Cornelia Lass-Florl

Ronald Gstir

Rafael Canton

Gilbert Greub

Danuta Cichocka

Affiliations

Soon will be listed here.

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.

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