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Determination of minimum inhibitory concentrations using machine-learning-assisted agar dilution.

Alessandro Gerada1,2, Nicholas Harper1, Alex Howard1,2

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Summary

AIgarMIC uses artificial intelligence to automate antimicrobial susceptibility testing, improving the accuracy and efficiency of minimum inhibitory concentration (MIC) measurements. This AI tool streamlines data collection for large-scale antimicrobial resistance surveillance programs.

Keywords:
antimicrobial resistanceartificial intelligenceassay validationdigital healthimage recognitionlaboratory softwaremachine learningminimum inhibitory concentration

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Area of Science:

  • Microbiology
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Antimicrobial resistance (AMR) poses a global health threat, necessitating accurate antimicrobial susceptibility data.
  • Traditional minimum inhibitory concentration (MIC) methods are labor-intensive, prone to error, and require significant infrastructure.
  • Standardizing MIC measurements is crucial for effective AMR surveillance and policy development.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model, AIgarMIC, for automating and standardizing MIC determination using agar dilution.
  • To assess the performance of AIgarMIC compared to manual interpretation of agar dilution tests.
  • To demonstrate a practical application of AI in clinical microbiology for enhanced data generation.

Main Methods:

  • Agar dilution was performed for 10 antibiotics against 1,086 clinical Enterobacterales isolates.
  • Photographs of inoculated agar plates were processed using a two-step convolutional neural network (CNN).
  • The first-step CNN identified bacterial growth, while the second-step CNN assessed antimicrobial inhibition based on colony morphology.

Main Results:

  • The first-step AI model achieved 94.3% accuracy in detecting bacterial growth.
  • The second-step AI model demonstrated 88.6% accuracy in classifying colony inhibition.
  • AIgarMIC achieved 98.9% essential agreement with manual MIC determination, with a bias of -7.8%.

Conclusions:

  • AIgarMIC effectively automates endpoint assessment for agar dilution MIC testing.
  • The AI tool has the potential to increase throughput and reduce laboratory barriers for generating high-quality MIC data.
  • AIgarMIC supports large-scale surveillance programs by providing standardized and reliable antimicrobial susceptibility data.