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.

由于样本复杂且需要快速筛查的样本量较大，早期识别食物、水和体液中的致病菌非常重要，但也具有挑战性。现有的基于平板计数或分子分析的筛选方法在检测时间、准确性/灵敏度、成本和样品制备复杂性方面存在各种权衡。在这里，我们提出了一种计算活细菌检测系统，该系统定期捕获 60 毫米直径琼脂板内细菌生长的相干显微镜图像，并使用深度神经网络分析这些延时全息图，以快速检测细菌生长并对细菌进行分类。对应的物种。我们的系统的性能通过快速检测水样中的大肠杆菌和总大肠菌（即产气克雷伯菌和肺炎克雷伯菌肺炎亚种）得到证明，与环境保护署（EPA）相比，检测时间缩短了>12小时)-批准的方法。通过在生长培养基中预孵育样品，我们的系统在 ≤ 9 小时的总测试时间内实现了约 1 菌落形成单位 (CFU)/L 的检测限 (LOD)。该平台具有极高的成本效益（约 0.6 美元/测试），并且具有高通量，整个板表面的扫描速度为 24 cm ² /min，非常适合与目前用于细菌检测的现有方法集成。琼脂平板。在深度学习的支持下，这种自动化且经济高效的活细菌检测平台可以显着减少检测时间并自动识别菌落，无需标记或不需要专家，可以为微生物学的广泛应用带来变革。