Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

Journal: Analytical chemistry
PMID:

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 human urine specimens spiked with lab strain E. coli (ATCC 43888) and an E. coli strain isolated from a clinical urine sample 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.

Authors

  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
  • Wenwen Jing
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
  • Rafael Iriya
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
  • Yunze Yang
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
  • Karan Syal
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
  • Manni Mo
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
  • Thomas E Grys
    Department of Laboratory Medicine and Pathology, Mayo Clinic , Phoenix , Arizona 85054 , United States.
  • Shelley E Haydel
    Biodesign Center for Immunotherapy, Vaccines, and Virotherapy , Arizona State University , Tempe , Arizona 85287 , United States.
  • ShaoPeng Wang
    School of Life Sciences, Shanghai University, Shanghai 200444, China.
  • Nongjian Tao
    Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.