Early detection and classification of live bacteria using holography and deep learning

Early identification of pathogenic bacteria in large volume and complex samples such as drinking water and bodily fluids is a major challenge. Traditional methods used to detect the viability of bacteria are based on plate counting or molecular analysis, and suffer from disadvantages in terms of the detection time, cost, and limited portability for use in field-settings. Here we present a live bacteria detection system that captures time-lapse holographic images of a 60 mm-diameter agar plate followed by differential image analysis and deep neural network-based processing for specific and sensitive detection of bacterial growth and classification of the growing species. We demonstrated the performance of our computational imaging system using water samples spiked with Escherichia coli and total coliform bacteria, and achieved >12 h time savings compared to the EPA-approved methods. Our system is label-free and is able to automatically detect ~1 colony-forming unit (CFU)/L in less than 9 h of total test time, including sample preparation, pre-incubation of the samples and automated image processing and colony counting. This label-free and high-throughput platform is cost-effective and field-portable, making it especially suitable for use in resource limited settings.