Deep learning accelerates the detection of live bacteria using thin-filmtransistor (TFT) arrays – a technology widely used in mobile phonedisplays

Early detection and identification of pathogenic bacteria in food and water samples
are essential to public health. Bacterial infections cause millions of deaths worldwide
and bring a heavy economic burden, costing more than 4 billion dollars annually in
the United States alone. Among pathogenic bacteria, Escherichia coli (E. coli) and
other coliform bacteria are among the most common ones, and they indicate fecal
contamination in food and water samples. The most conventional and frequently used
method for detecting these bacteria involves culturing of the samples, which usually
takes >24 hours for the final read-out and needs expert visual examination. Although
some methods based on, for example, the amplification of nucleic acids, can reduce
the detection time to a few hours, they cannot differentiate live and dead bacteria and
present low sensitivity at low concentrations of bacteria. That is why the US
Environmental Protection Agency (EPA) approves no nucleic acid-based bacteria
sensing method for screening water samples.
In an article recently published in ACS Photonics, a journal of the American Chemical
Society (ACS), a team of scientists, led by Professor Aydogan Ozcan from the
Electrical and Computer Engineering Department at the University of California, Los
Angeles (UCLA), and co-workers have developed an AI-powered smart bacterial
colony detection system using a thin-film transistor (TFT) array, which is a widely
used technology in mobile phones and other displays. The ultra-large imaging area of
the TFT array (27 mm × 26 mm) manufactured by researchers at Japan Display Inc.
enabled the system to rapidly capture the growth patterns of bacterial colonies without
the need for scanning, which significantly simplified both the hardware and software
design. This system achieved ~12-hour time savings compared to gold-standard
culture-based methods approved by EPA. By analyzing the microscopic images
captured by the TFT array as a function of time, the AI-based system could rapidly
and automatically detect colony growth with a deep neural network. Following the
detection of each colony, a second neural network is used to classify the bacteria
The efficacy of this automated bacterial colony detection system was demonstrated by
performing early detection and classification of three types of bacteria, i.e., E. coli,
Citrobacter, and Klebsiella pneumoniae (K. pneumoniae). The researchers achieved a
colony detection rate of >90% within 9 hours and further identified their species at
∼12 hours, corresponding to a time saving of ~12 hours compared to the EPA-
approved culture methods. In addition, all the digital processing steps take <25 sec
using a standard computer without needing an advanced graphics processing unit
These results demonstrate the feasibility of this automated, AI-based bacterial colony
detection system using TFT arrays as a rapid, cost-effective, and accurate technique,
which is especially suitable for resource-limited environments. Due to the low-cost,
low-heat generation, large scalability, and low power consumption of TFT arrays
widely used in mobile displays, this automated colony detection platform has massive
potential to be used in microbiology research and field-based bacteria sensing.

See the article:
Deep learning-enabled detection and classification of bacterial colonies using a thin-
film transistor (TFT) image sensor, published in ACS Photonics