Abstract

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.

Original languageEnglish (US)
Pages (from-to)6314-6322
Number of pages9
JournalUnknown Journal
Volume90
Issue number10
DOIs
StatePublished - May 15 2018

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ASJC Scopus subject areas

  • Analytical Chemistry

Cite this

Yu, H., Jing, W., Iriya, R., Yang, Y., Syal, K., Mo, M., Grys, T. E., Haydel, S., Wang, S., & Tao, N. (2018). Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy. Unknown Journal, 90(10), 6314-6322. https://doi.org/10.1021/acs.analchem.8b01128