Deep Learning-Enabled Detection and Classification of Bacterial Colonies Using a Thin-Film Transistor (TFT) Image SensorClick to copy article linkArticle link copied!
- Yuzhu LiYuzhu LiElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesBioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Yuzhu Li
- Tairan LiuTairan LiuElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesBioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Tairan Liu
- Hatice Ceylan KoydemirHatice Ceylan KoydemirDepartment of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United StatesCenter for Remote Health Technologies and Systems, Texas A&M University, College Station, Texas 77843, United StatesMore by Hatice Ceylan Koydemir
- Hongda WangHongda WangElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesBioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Hongda Wang
- Keelan O’RiordanKeelan O’RiordanDepartment of Physics and Astronomy, University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Keelan O’Riordan
- Bijie BaiBijie BaiElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesBioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Bijie Bai
- Yuta HagaYuta HagaSystem Development Department, Research & Development Division, Japan Display Inc., Kanagawa 243-0432, JapanMore by Yuta Haga
- Junji KobashiJunji KobashiSystem Development Department, Research & Development Division, Japan Display Inc., Kanagawa 243-0432, JapanMore by Junji Kobashi
- Hitoshi TanakaHitoshi TanakaDevice Development Department, Research & Development Division, Japan Display Inc., Chiba 297-8622, JapanMore by Hitoshi Tanaka
- Takaya TamaruTakaya TamaruDevice Development Department, Research & Development Division, Japan Display Inc., Chiba 297-8622, JapanMore by Takaya Tamaru
- Kazunori YamaguchiKazunori YamaguchiR&D Planning Department, Research & Development Division, Japan Display Inc., Tokyo 105-0003, JapanMore by Kazunori Yamaguchi
- Aydogan Ozcan*Aydogan Ozcan*Email: [email protected]Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesBioengineering Department, University of California, Los Angeles, Los Angeles, California 90095, United StatesCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, California 90095, United StatesDepartment of Surgery, University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Aydogan Ozcan
Abstract
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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