Deep learning-based detection of bacterial swarm motion using a single image.

Journal: Gut microbes
PMID: 40366861

Abstract

Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. Conventionally, the detection of bacterial swarming involves inoculating samples on an agar surface and observing colony expansion, which is qualitative, time-intensive, and requires additional testing to rule out other motility forms. A recent methodology that differentiates swarming and swimming motility in bacteria using circular confinement offers a rapid approach to detecting swarming. However, it still heavily depends on the observer's expertise, making the process labor-intensive, costly, slow, and susceptible to inevitable human bias. To address these limitations, we developed a deep learning-based swarming classifier that rapidly and autonomously predicts swarming probability using a single blurry image. Compared with traditional video-based, manually processed approaches, our method is particularly suited for high-throughput environments and provides objective, quantitative assessments of swarming probability. The swarming classifier demonstrated in our work was trained on . SM3 and showed good performance when blindly tested on new swarming (positive) and swimming (negative) test images of SM3, achieving a sensitivity of 97.44% and a specificity of 100%. Furthermore, this classifier demonstrated robust external generalization capabilities when applied to unseen bacterial species, such as DB10 and H6. This competitive performance indicates the potential to adapt our approach for diagnostic applications through portable devices, which would facilitate rapid, objective, on-site screening for bacterial swarming motility, potentially enhancing the early detection and treatment assessment of various diseases, including inflammatory bowel diseases (IBD) and urinary tract infections (UTI).

Authors

  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Weijie Chen
  • Keelan O'Riordan
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Neha Mani
    Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
  • Yuxuan Qi
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Sridhar Mani
    Department of Medicine, Genetics and Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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