抗菌剂
抗生素
尿
细菌
深度学习
微生物学
金标准(测试)
泌尿系统
最小抑制浓度
人工智能
化学
医学
生物
内科学
计算机科学
遗传学
作者
Hui Yu,Wenwen Jing,Rafael Iriya,Yuanyuan Yang,Karan Syal,Manni Mo,Thomas E. Grys,Shelley E. Haydel,Shaopeng Wang,Nongjian Tao
标识
DOI:10.1021/acs.analchem.8b01128
摘要
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.
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