Deep learning enables high-throughput early detection and classification of bacterial colonies using time-lapse coherent imaging (Conference Presentation)

We report a highly-sensitive, high-throughput, and cost-effective bacteria identification system which continuously captures and reconstructs holographic images of an agar-plate and analyzes the time-lapsed images with deep learning models for early detection of colonies. The performance of our system was confirmed by detection and classification of Escherichia coli, Enterobacter aerogenes, and Klebsiella pneumoniae in water samples. We detected 90% of the bacterial colonies and their growth within 7-10h (>95% within 12h) with ~100% precision, and correctly identified the corresponding species within 7.6-12h with 80% accuracy, and achieved time savings of >12h as compared to the gold-standard EPA-approved methods.