Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Hongda Wang; Hatice Ceylan Koydemir; Yunzhe Qiu; Bijie Bai; Yibo Zhang; Yiyin Jin; Sabiha Tok; Enis Cagatay Yilmaz; Esin Gumustekin; Yair Rivenson; Aydogan Ozcan

doi:10.1038/s41377-020-00358-9

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Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Original Articles

- Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning
- Light: Science & Applications Vol. 9, Issue 4, Pages: 1150-1166(2020)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
  4.Department of Biophysics, Istanbul Medical Faculty, Istanbul University, Istanbul 22000, Turkey
  5.Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
  6.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-020-00358-9
  CLC：
- Published：2020，
  
  Published Online：10 July 2020，
  
  Received：18 April 2020，
  
  Revised：20 June 2020，
  
  Accepted：22 June 2020
- Accepted：
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Wang, H. D. et al. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light: Science & Applications, 9, 1150-1166 (2020).
DOI：

Wang, H. D. et al. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light: Science & Applications, 9, 1150-1166 (2020). DOI： 10.1038/s41377-020-00358-9.

摘要

Abstract

Early identificatio

n of pathogenic bacteria in food

water

and bodily fluids is very important and yet challenging

owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time

accuracy/sensitivity

cost

and sample preparation complexity. Here

we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of

Escherichia coli

and total coliform bacteria (i.e.

Klebsiella aerogenes

and

Klebsiella pneumoniae

subsp

. pneumoniae

) in water samples

shortening the detection time by

12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media

our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~＄0.6/test) and has high-throughput with a scanning speed of 24 cm

/min over the entire plate surface

making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning

this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

关键词

Keywords

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