Publication detail

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

LEPÍK, J. ČIČATKA, M.

Original Title

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

Type

article in a collection out of WoS and Scopus

Language

English

Original 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

Authors

LEPÍK, J.; ČIČATKA, M.

Released

25. 4. 2024

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

Pages from

20

Pages to

23

Pages count

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf

BibTex

@inproceedings{BUT188474,
  author="Jakub {Lepík} and Michal {Čičatka}",
  title="Automating Antibiotic Susceptibility Testing with Machine Learning for Disk Diffusion Test Analysis",
  year="2024",
  pages="20--23",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf"
}