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

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 cm 2 /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.

“NAD‐display”: Ultrahigh‐Throughput in Vitro Screening of NAD(H) Dehydrogenases Using Bead Display and Flow Cytometry

“…As we were able to flow cytometrically establish the genotype of each bead, we could determine that the sorting gate as indicated (Figure 3 D), would lead to an enrichment of 312‐fold. In addition, the fact that all possible events could be accounted for, including true positives and false positives, enabled the positive predictive value [28] of the screen to be calculated: when applying this sorting gate, it was found to be 99.1 %. Even accounting for potential mis‐sorting events caused by a sorting flow cytometer, this established an excellent maximal enrichment.…”

“NAD‐display”: Ultrahigh‐Throughput in Vitro Screening of NAD(H) Dehydrogenases Using Bead Display and Flow Cytometry

“…As we were able to flow cytometricallyestablish the genotype of each bead, we could determine that the sorting gate as indicated (Figure 3D), would lead to an enrichment of 312-fold. In addition, the fact that all possible events could be accounted for, including true positives and false positives, enabled the positive predictive value [28] of the screen to be calculated:when applying this sorting gate,itwas found to be 99.1 %. Even accounting for potential mis-sorting events caused by as orting flow cytometer,t his established an excellent maximal enrichment.…”

Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

“…Recent advances in artificial intelligence (AI) have suggested unexplored domains of QPI beyond simply characterizing biological samples [22]. As datasets obtained from QPI do not rely on the variability of staining quality, various machine learning and deep learning approaches can exploit uniform-quality and high-dimensional datasets to perform label-free image segmentation [23,24], classification [25][26][27][28][29][30][31][32], and inference [33][34][35][36][37][38][39]. Such synergetic approaches for label-free blood cell identification have also been demonstrated, which are of interest to this work [25,26,28,[40][41][42][43].…”

“…As datasets obtained from QPI do not rely on the variability of staining quality, various machine learning and deep learning approaches can exploit uniform-quality and high-dimensional datasets to perform label-free image segmentation [23,24], classification [25][26][27][28][29][30][31][32], and inference [33][34][35][36][37][38][39]. Such synergetic approaches for label-free blood cell identification have also been demonstrated, which are of interest to this work [25,26,28,[40][41][42][43]. However, these often necessitate manual extraction of features for machine learning or do not fully utilize the high-complexity data of three-dimensional (3D) QPI, possibly improving the performance of deep learning.…”