Detail publikace

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

LEPÍK, J. ČIČATKA, M.

Originální název

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

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

LEPÍK, J.; ČIČATKA, M.

Vydáno

25. 4. 2024

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

Strany od

20

Strany do

23

Strany počet

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"
}