Automating Antibiotic Susceptibility Testing with Machine Learning for Disk Diffusion Test Analysis

Lepík, Jakub; Čičatka, Michal

Automating Antibiotic Susceptibility Testing with Machine Learning for Disk Diffusion Test Analysis

Files

20-eeict-2024.pdf(3.69 MB)

Date

2024

Authors

Lepík, Jakub

Čičatka, Michal

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Abstract

Rapid and reliable antibiotic susceptibility testing (AST) methods are imperative in response to the escalating challenges of antimicrobial resistance. This study focuses on enhancing disk diffusion testing, a cornerstone of AST, by integrating machine learning and automation. Leveraging state-of-the-art object detection models, including EfficientDet and Mask R-CNN and image-processing approaches, our methodology addresses the need for standardized evaluation processes across diverse laboratory equipment while enabling the integration of mobile devices into the workflow, democratizing AST, and enhancing its accessibility. We utilize a comprehensive disk diffusion dataset for object detection models captured by devices like mobile phones and professional solutions. Additionally, our experiments lay the groundwork for a web application adopting a device-agnostic approach, promising improved accessibility and efficiency in AST analysis.

Keywords

antibiotic sensitivity testing, disk diffusion test, machine learning, image processing

Citation

Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 20-23. ISBN 978-80-214-6231-1
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence