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

Abstract

Effective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human error, and require considerable infrastructure. AIgarMIC streamlines and standardizes MIC measurement and is especially valuable for large-scale surveillance activities. MICs were measured using agar dilution for n = 10 antibiotics against clinical Enterobacterales isolates (n = 1,086) obtained from a large tertiary hospital microbiology laboratory. Escherichia coli (n = 827, 76%) was the most common organism. Photographs of agar plates were divided into smaller images covering one inoculation site. A labeled data set of colony images was created and used to train a convolutional neural network to classify images based on whether a bacterial colony was present (first-step model). If growth was present, a second-step model determined whether colony morphology suggested antimicrobial growth inhibition. The ability of the AI to determine MIC was then compared with standard visual determination. The first-step model classified bacterial growth as present/absent with 94.3% accuracy. The second-step model classified colonies as "inhibited" or "good growth" with 88.6% accuracy. For the determination of MIC, the rate of essential agreement was 98.9% (644/651), with a bias of -7.8%, compared with manual annotation. AIgarMIC uses artificial intelligence to automate endpoint assessments for agar dilution and potentially increases throughput without bespoke equipment. AIgarMIC reduces laboratory barriers to generating high-quality MIC data that can be used for large-scale surveillance programs.

Importance: This research uses modern artificial intelligence and machine-learning approaches to standardize and automate the interpretation of agar dilution minimum inhibitory concentration testing. Artificial intelligence is currently of significant topical interest to researchers and clinicians. In our manuscript, we demonstrate a use-case in the microbiology laboratory and present validation data for the model's performance against manual interpretation.

Keywords: antimicrobial resistance; artificial intelligence; assay validation; digital health; image recognition; laboratory software; machine learning; minimum inhibitory concentration.

Fig 1

A summary of the steps required to develop AIgarMIC, where c is the number of control (negative) plates, k is the number of concentrations per antimicrobial, a is the number of antimicrobials, x is the total number of plates required, and m is the number of strains/spots per plate (up to 96 in our study).

Fig 2

Overview of study methodology and design. *Strains that failed to grow, or agar plates in which the control strain E. coli 25922 MIC was outside the expected range were excluded, †no growth vs growth, ‡poor/inhibited growth vs good growth.

Fig 3

Heterogeneity of colony phenotypic morphology. No growth (top row) can include agar and imaging artifacts. Antimicrobial-inhibited growth (middle row) includes single colony (left), discrete inhibited colonies (middle), and faint film of growth (right). Growth of colonies in the bottom row is not inhibited by the tested antimicrobial.

Fig 4

Confusion matrix for the performance of both models on the validation colony image dataset. The first-step model determines whether any bacterial growth is present in the image. The second-step model determines the quality of the growth.

Fig 5

Validation summary for AIgarMIC against manual annotation on E. coli strains. Red triangles in right-hand plot indicate MICs that failed essential agreement. The diagonal line is the line of identity (i.e., intercept = 0, slope = 1).

Fig 6

Heatmap of MIC results for AIgarMIC against manual annotation on E. coli strains, EA, essential agreement.