Deep Learning and Single Cell Phenotyping for Rapid Antimicrobial Susceptibility Testing

The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current gold-standard antimicrobial susceptibility tests (ASTs) are low-throughput and can take up to 48 hours, with implications for patient care. We present advances towards a novel, rapid AST, based on the deep-learning of single-cell specific phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells, across a range of antibiotics and phenotypes - including phenotypes not visually distinct to a trained, human observer. Applying models trained on lab-reference susceptible strains to clinical isolates of E. coli treated with ciprofloxacin, we demonstrate our models reveal significant (p<0.001) differences between resistant and susceptible populations, around a fixed treatment level. Conversely, deploying on cells treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to a 24-hour growth AST assay, but in as little as 30 minutes.