AIgarMIC is a Python package and collection of commandline scripts designed to facilitate the automation of agar dilution minimum inhibitory concentration image interpretation.
AIgarMIC has the following features:
- Automated image processing of agar dilution plates in the following format (note the use of an anchoring black grid to delineate colonies):
- Flexible MIC calculation algorithm with ability to disregard inhibited growth
- Quality assurance metrics to ensure MIC predictions
- Pre-trained models and example datasets
- Scripts to support custom model training
The full documentation for AIgarMIC can be found at:
https://aigarmic.readthedocs.io/en/latest/
To install AIgarMIC, follow the instructions below:
https://aigarmic.readthedocs.io/en/latest/installation.html
To use AIgarMIC, follow one of the typical workflows described below:
https://aigarmic.readthedocs.io/en/latest/introduction.html#typical-workflows
The lead developer of AIgarMIC is Alessandro Gerada (https://github.com/agerada/ and https://agerada.github.io/),
University of Liverpool, UK ([email protected]).
If you are using AIgarMIC in your research project, please cite:
@article{geradaAIgarMICPythonPackage2024,
title = {{{AIgarMIC}}: A {{Python}} Package for Automated Interpretationof Agar Dilution Minimum Inhibitory Concentration Assays},
shorttitle = {{{AIgarMIC}}},
author = {Gerada, Alessandro and Harper, Nicholas and Howard, Alex and Hope, William},
year = {2024},
month = sep,
journal = {Journal of Open Source Software},
volume = {9},
number = {101},
pages = {6826},
issn = {2475-9066},
doi = {10.21105/joss.06826},
urldate = {2024-10-07},
copyright = {http://creativecommons.org/licenses/by/4.0/},
file = {/Users/agerada/Library/Mobile Documents/com~apple~CloudDocs/Zotero/Journal Article/Gerada et al_2024_AIgarMIC.pdf}
}
To cite the validation data and developmental approach described in the AIgarMIC validation manuscript, please cite:
@article{geradaDeterminationMinimumInhibitory2024,
title = {Determination of Minimum Inhibitory Concentrations Using Machine-Learning-Assisted Agar Dilution},
author = {Gerada, Alessandro and Harper, Nicholas and Howard, Alex and Reza, Nada and Hope, William},
editor = {Shier, Kileen L.},
date = {2024-03-22},
journaltitle = {Microbiology Spectrum},
shortjournal = {Microbiol Spectr},
pages = {e04209-23},
issn = {2165-0497},
doi = {10.1128/spectrum.04209-23},
url = {https://journals.asm.org/doi/10.1128/spectrum.04209-23},
urldate = {2024-04-02},
langid = {english}
}
The manuscript describing the validation of AIgarMIC can be found at: https://doi.org/10.1128/spectrum.04209-23.
Optional asset data is available at: https://doi.org/10.17638/datacat.liverpool.ac.uk%2F2631.
We welcome contributions to AIgarMIC. Please follow our contributing guidelines.
AIgarMIC is provided under the GNU General Public License v3.0. For more information, see the LICENSE file.