Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy
| Autor: | Manni Mo, Shelley E. Haydel, Nongjian Tao, Rafael Iriya, Hui Yu, Karan Syal, Thomas E. Grys, Wenwen Jing, Yunze Yang, Shaopeng Wang |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
medicine.drug_class Antibiotics Antimicrobial susceptibility Video microscopy Urine Microbial Sensitivity Tests 01 natural sciences Analytical Chemistry Microbiology 03 medical and health sciences Minimum inhibitory concentration Antibiotic resistance Deep Learning medicine Humans Microscopy Video biology Chemistry 010401 analytical chemistry Gold standard (test) biology.organism_classification 0104 chemical sciences Anti-Bacterial Agents 030104 developmental biology Phenotype Urinary Tract Infections Bacteria |
| Zdroj: | Analytical chemistry. 90(10) |
| ISSN: | 1520-6882 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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