  Early detection and identification of pathogenic bacteria such as Escherichia
coli (E. coli) is an essential task for public health. The conventional
culture-based methods for bacterial colony detection usually take >24 hours to
get the final read-out. Here, we demonstrate a bacterial colony-forming-unit
(CFU) detection system exploiting a thin-film-transistor (TFT)-based image
sensor array that saves ~12 hours compared to the Environmental Protection
Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU
detection system, a lensfree imaging modality was built using the TFT image
sensor with a sample field-of-view of ~10 cm^2. Time-lapse images of bacterial
colonies cultured on chromogenic agar plates were automatically collected at
5-minute intervals. Two deep neural networks were used to detect and count the
growing colonies and identify their species. When blindly tested with 265
colonies of E. coli and other coliform bacteria (i.e., Citrobacter and
Klebsiella pneumoniae), our system reached an average CFU detection rate of
97.3% at 9 hours of incubation and an average recovery rate of 91.6% at ~12
hours. This TFT-based sensor can be applied to various microbiological
detection methods. Due to the large scalability, ultra-large field-of-view, and
low cost of the TFT-based image sensors, this platform can be integrated with
each agar plate to be tested and disposed of after the automated CFU count. The
imaging field-of-view of this platform can be cost-effectively increased to
>100 cm^2 to provide a massive throughput for CFU detection using, e.g.,
roll-to-roll manufacturing of TFTs as used in the flexible display industry.
