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Therefore, there is an urgent need for an automated method that can achieve rapid and high-throughput bacterial colony detection with high sensitivity to provide a powerful alternative to the currently available EPA-approved gold-standard methods that take at least 24 hours and require an expert for colony counting.
In a new paper published in Light: Science & Applications, a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), USA, and co-workers have developed an AI-powered smart imaging system for early-detection and classification of live bacteria in water samples. Based on holography, they designed a highly sensitive and high-throughput imaging system, which continuously captures microscopic images of a whole culture plate, where bacteria grow, to rapidly detect colony growth by analyzing these time-lapse images with a deep neural network. Following the detection of each colony growth, a second neural network is used to classify the type of bacteria.
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The efficacy of this unique platform was demonstrated by performing early detection and classification of three types of bacteria, i.e., E. coli, Klebsiella aerogenes (K. aerogenes), and Klebsiella pneumoniae (K. pneumoniae), and the UCLA researchers achieved a limit-of-detection of 1 colony forming bacterium per 1 Liter of water sample under 9 hours of total test time, demonstrating a time saving of more than 12 hours for bacteria detection as compared to the gold-standard EPA methods. These results highlight the transformative potential of this AI-powered holographic imaging platform, which not only enables highly sensitive, rapid and cost-effective detection of live bacteria, but also provides a powerful and versatile tool for microbiology research.
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