Optimization of MIC calls

To optimize the ability to call MICs based on ConvNet output, we collected a separate dataset, independent of the training and validation datasets, which we refer to as the optimization dataset. We used the MAST assay to collect images from all combinations of antibiotics and organisms listed in “Bacterial strains and antimicrobials” on three separate days (180 MIC assays x 10 antibiotic concentrations = 1800 images). Images in the optimization dataset were evaluated using our trained ConvNet and all output parameters were recorded (mean image inhibition probability, median inhibition probability, and proportion of crops with inhibition probability >0.5).

For each output parameter, a custom Python script was used to model results from each dilution series as a sigmoidal curve of the form:

For each antibiotic/parameter combination, we iteratively set thresholds (ranging between 0 and 0.99) that represented the point on the sigmoidal curve above which all results would be called “inhibited”. We then calculated MIC accuracy at each threshold and identified the optimal parameter and threshold combination that resulted in the highest MIC accuracy. If the highest accuracy was achieved at multiple potential threshold values for a given parameter, the median of those values was designated as the optimal threshold cutoff value.