Image dataset of disk diffusion assay scanned with the SIRscan system

We present a comprehensive dataset deposited in the DRYAD repository,
which includes high-resolution images and corresponding automated
interpretations from the SIRscan system. This dataset is intended to
support the development and validation of machine learning models and
other analytical tools aimed at enhancing the accuracy of antimicrobial
resistance detection, particularly in Gram-negative bacteria. The images
in this dataset were generated using disk diffusion methods following the
EUCAST guidelines and encompass a variety of phenotypic resistance
patterns against beta-lactam antibiotics. The dataset includes 225
Gram-negative bacterial isolates with a total of 862 unique phenotypic
categories, reflecting various resistance mechanisms, including
extended-spectrum beta-lactamase (ESBL), plasmid-mediated AmpC
beta-lactamase, and carbapenemase production. Each image is paired with an
automated reading provided by the SIRscan system, which includes
measurements of inhibition zone diameters and preliminary resistance
classification. This pairing of raw image data with machine-generated
interpretations offers a valuable resource for the development of advanced
algorithms for antimicrobial resistance prediction and other related
applications. This dataset is part of an ongoing effort to provide
open-access resources that can be used to benchmark and validate the
performance of machine learning models in clinical microbiology. By
sharing these data, we aim to facilitate the development of more accurate
and efficient diagnostic tools, ultimately contributing to better clinical
outcomes and more effective antimicrobial stewardship.