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Deep Learning-Enabled Detection and Classification of Bacterial Colonies Using a Thin-Film Transistor (TFT) Image Sensor

  • Yuzhu Li
    Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United States
    More by Yuzhu Li
  • Tairan Liu
    Tairan Liu
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United States
    More by Tairan Liu
  • Hatice Ceylan Koydemir
    Hatice Ceylan Koydemir
    Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States
    Center for Remote Health Technologies and Systems, Texas A&M University, College Station, Texas 77843, United States
  • Hongda Wang
    Hongda Wang
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United States
    More by Hongda Wang
  • Keelan O’Riordan
    Keelan O’Riordan
    Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, California 90095, United States
  • Bijie Bai
    Bijie Bai
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United States
    More by Bijie Bai
  • Yuta Haga
    Yuta Haga
    System Development Department, Research & Development Division, Japan Display Inc., Kanagawa 243-0432, Japan
    More by Yuta Haga
  • Junji Kobashi
    Junji Kobashi
    System Development Department, Research & Development Division, Japan Display Inc., Kanagawa 243-0432, Japan
  • Hitoshi Tanaka
    Hitoshi Tanaka
    Device Development Department, Research & Development Division, Japan Display Inc., Chiba 297-8622, Japan
  • Takaya Tamaru
    Takaya Tamaru
    Device Development Department, Research & Development Division, Japan Display Inc., Chiba 297-8622, Japan
  • Kazunori Yamaguchi
    Kazunori Yamaguchi
    R&D Planning Department, Research & Development Division, Japan Display Inc., Tokyo 105-0003, Japan
  • , and 
  • Aydogan Ozcan*
    Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United States
    California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United States
    Department of Surgery, University of California, Los Angeles, Los Angeles, California 90095, United States
    *Email: [email protected]
Cite this: ACS Photonics 2022, 9, 7, 2455–2466
Publication Date (Web):June 30, 2022
https://doi.org/10.1021/acsphotonics.2c00572
Copyright © 2022 American Chemical Society

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    Abstract

    Abstract Image

    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 h to get the final readout. Here, we demonstrate a bacterial colony-forming-unit (CFU) detection system exploiting a thin-film-transistor (TFT)-based image sensor array that saves ∼12 h compared to the Environmental Protection Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU detection system, a lens-free imaging modality was built using the TFT image sensor with a sample field-of-view of ∼7 cm2. Time-lapse images of bacterial colonies cultured on chromogenic agar plates were automatically collected at 5 min 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 h of incubation and an average recovery rate of 91.6% at ∼12 h. 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 cm2 to provide a massive throughput for CFU detection using, e.g., roll-to-roll manufacturing of TFTs, as used in the flexible display industry.

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsphotonics.2c00572.

    • Video to exemplify the automated detection and classification performance for E. coli colonies (Video S1) (MP4)

    • Video to exemplify the automated detection and classification performance for Citrobacter colonies (Video S2) (MP4)

    • Video to exemplify the automated detection and classification performance for K. pneumoniae colonies (Video S3) (MP4)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 1 publications.

    1. Yuzhu Li, Tairan Liu, Hatice Ceylan Koydemir, Hongda Wang, Keelan O’Riordan, Bijie Bai, Yuta Haga, Junji Kobashi, Hitoshi Tanaka, Takaya Tamaru, Kazunori Yamaguchi, Aydogan Ozcan. Early detection and classification of bacterial colonies using a Thin-Film-Transistor (TFT)-based image sensor and deep learning. 2022, FW6E.2. https://doi.org/10.1364/FIO.2022.FW6E.2

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