查看文章

Multidrug-resistant bacteria are among the most urgent global public health threats. Rapid determination of antimicrobial resistance in a single bacterium is a major clinical unmet need in the diagnosis of bacterial infections.

By capturing dynamic single-cell morphological features of over twenty-eight thousand cells, we evaluated strategies based on time and concentration differentials for classifying the susceptibility of Klebsiella pneumoniae to meropenem and predicting their minimum inhibitory concentrations (MIC).

The classifiers achieved as high as 97% accuracy in 20 minutes (two-fifths of the doubling time) and reached over 99% accuracy within 50 minutes (one doubling time) in predicting the antimicrobial response. A regression model based on the concentration differential of individual cells predicted the MIC with >97% categorical agreement and 100% essential agreement. When tested against cells from an unseen strain, the regressor achieved a categorical agreement of 91.9% with a very major error of 0.1%.

We report morphometric antimicrobial susceptibility testing (MorphoAST), an image-based machine learning workflow, for rapid determination of antimicrobial susceptibility by single-cell morphological analysis in a sub-doubling time. Our approach has the ability to predict bacterial antimicrobial responsiveness in a fraction of the organisms’ doubling time. This innovation will have significant implications for the future management of bacterial infections.

This work was supported in part by NIH NIAID (R01AI153133).

Classic antimicrobial …