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Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of *Salmonella Typhimurium*

Medicine and Health

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of *Salmonella Typhimurium*

T. Tran, S. Sridhar, et al.

Discover how researchers, including Tuan-Anh Tran and Sushmita Sridhar, are tackling the emerging threat of antimicrobial resistance (AMR) using innovative machine learning techniques. This study reveals a groundbreaking approach to predict ciprofloxacin susceptibility in *Salmonella Typhimurium*, offering the potential for rapid AMR detection.

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Playback language: English
Abstract
Antimicrobial resistance (AMR) is a growing public health crisis. Current susceptibility testing is slow. This study uses high-content imaging and machine learning to rapidly predict ciprofloxacin susceptibility in *Salmonella Typhimurium*. Isolates showed distinct growth and morphology, clustering by susceptibility. Machine learning accurately predicted susceptibility without ciprofloxacin exposure, demonstrating the potential of this technique for rapid AMR prediction.
Publisher
Nature Communications
Published On
Jun 13, 2024
Authors
Tuan-Anh Tran, Sushmita Sridhar, Stephen T. Reece, Octavie Lunguya, Jan Jacobs, Sandra Van Puyvelde, Florian Marks, Gordon Dougan, Nicholas R. Thomson, Binh T. Nguyen, Pham The Bao, Stephen Baker
Tags
antimicrobial resistance
ciprofloxacin
machine learning
Salmonella Typhimurium
susceptibility testing
high-content imaging

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