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

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. A novel automated system quickly detects and classifies live bacteria in water by using deep neural networks to analyze holographic images. Water-borne pathogens affect billions of people, but current gold standard methods for counting and identifying live bacteria in water take 24 hours or more, highlighting the need for fast, accurate, automatic methods that can handle large sample sizes. Aydogan Ozcan at the University of California Los Angeles, USA, and co-workers developed a system that analyzes lensfree holographic microscopy images of bacteria growing on agar plates. After training and testing their algorithms with >16000 bacterial colonies from three different species, the team was able to detect bacterial growth and classify species in <12 hours. The system will not only improve monitoring of food and water quality, but also provides a powerful tool for microbiology research.