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1 August 2021 Early identification of live bacteria in water samples using time-lapse holographic imaging and deep learning
Hongda Wang, Hatice C. Koydemir, Yunzhe Qiu, Bijie Bai, Yibo Zhang, Yiyin Jin, Sabiha Tok, Enis C. Yilmaz, Esin Gumustekin, Yair Rivenson, Aydogan Ozcan
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041T (2021) https://doi.org/10.1117/12.2593804
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
We present a deep learning-aided imaging system for early detection and classification of live bacterial colonies by capturing time-lapse holographic images of an agar plate and analyzing these images using deep neural networks. We blindly tested our system by identifying Escherichia coli and total coliform bacteria in spiked water samples and successfully detected 90% of the bacterial colonies within 7-10 h, while keeping 99.2~100% precision. We further classified the corresponding species within 7.6-12 h of incubation with 80% accuracy, which represents >12 h time-savings. Our system also achieved a limit-of-detection of ~1 CFU/L within 9 h of total test time.
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Hongda Wang, Hatice C. Koydemir, Yunzhe Qiu, Bijie Bai, Yibo Zhang, Yiyin Jin, Sabiha Tok, Enis C. Yilmaz, Esin Gumustekin, Yair Rivenson, and Aydogan Ozcan "Early identification of live bacteria in water samples using time-lapse holographic imaging and deep learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041T (1 August 2021); https://doi.org/10.1117/12.2593804
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KEYWORDS
Bacteria
Holography
Statistical analysis
Image classification
Imaging systems
Microbiology
Neural networks
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