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

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

Light Sci Appl. 2020 Jul 10:9:118. doi: 10.1038/s41377-020-00358-9. eCollection 2020.

Authors

Hongda Wang^#^{1

2

3}, Hatice Ceylan Koydemir^#^{1

2

3}, Yunzhe Qiu^#^{1

2

3}, Bijie Bai^{1

2

3}, Yibo Zhang^{1

2

3}, Yiyin Jin¹, Sabiha Tok^{1

2

3

4}, Enis Cagatay Yilmaz¹, Esin Gumustekin⁵, Yair Rivenson^{1

2

3}, Aydogan Ozcan^{1

2

3

6}

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.

^# Contributed equally.

Abstract

Early identification 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 cm²/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.

Keywords: Biophotonics; Imaging and sensing; Microscopy.