AIgarMIC

Introduction

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

Documentation

The full documentation for AIgarMIC can be found at:

https://aigarmic.readthedocs.io/en/latest/

Installation

To install AIgarMIC, follow the instructions below:

https://aigarmic.readthedocs.io/en/latest/installation.html

Usage

To use AIgarMIC, follow one of the typical workflows described below:

https://aigarmic.readthedocs.io/en/latest/introduction.html#typical-workflows

Author information

The lead developer of AIgarMIC is Alessandro Gerada (https://github.com/agerada/ and https://agerada.github.io/), University of Liverpool, UK (alessandro.gerada@liverpool.ac.uk).

Cite

If you are using AIgarMIC in your research project, please cite [TO FOLLOW].

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}
}

External links

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://10.17638/datacat.liverpool.ac.uk/2631.

Contributing

We welcome contributions to AIgarMIC. Please follow our contributing guidelines.

License

AIgarMIC is provided under the GNU General Public License v3.0. For more information, see the LICENSE file.