Deep learning-based detection of bacterial swarm motion using a single image
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Gut Microbes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19490976.2025.2505115 |
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| author | Yuzhu Li Hao Li Weijie Chen Keelan O’Riordan Neha Mani Yuxuan Qi Tairan Liu Sridhar Mani Aydogan Ozcan |
| author_facet | Yuzhu Li Hao Li Weijie Chen Keelan O’Riordan Neha Mani Yuxuan Qi Tairan Liu Sridhar Mani Aydogan Ozcan |
| author_sort | Yuzhu Li |
| collection | DOAJ |
| description | 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 Enterobacter sp. 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 Serratia marcescens DB10 and Citrobacter koseri 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). |
| format | Article |
| id | doaj-art-cf67546e7dd14e9cb0ef311237c6f9d9 |
| institution | OA Journals |
| issn | 1949-0976 1949-0984 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Gut Microbes |
| spelling | doaj-art-cf67546e7dd14e9cb0ef311237c6f9d92025-08-20T02:24:55ZengTaylor & Francis GroupGut Microbes1949-09761949-09842025-12-0117110.1080/19490976.2025.2505115Deep learning-based detection of bacterial swarm motion using a single imageYuzhu Li0Hao Li1Weijie Chen2Keelan O’Riordan3Neha Mani4Yuxuan Qi5Tairan Liu6Sridhar Mani7Aydogan Ozcan8Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USADepartment of Medicine, Genetics and Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USADepartment of Medicine, Genetics and Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USAElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USADepartment of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USAElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USAElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USADepartment of Medicine, Genetics and Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USAElectrical and Computer Engineering Department, University of California, Los Angeles, CA, USAMotility 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 Enterobacter sp. 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 Serratia marcescens DB10 and Citrobacter koseri 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).https://www.tandfonline.com/doi/10.1080/19490976.2025.2505115Bacterial motilitydeep learningswarminginflammatory bowel diseasein vitro diagnosismicrobiome test |
| spellingShingle | Yuzhu Li Hao Li Weijie Chen Keelan O’Riordan Neha Mani Yuxuan Qi Tairan Liu Sridhar Mani Aydogan Ozcan Deep learning-based detection of bacterial swarm motion using a single image Gut Microbes Bacterial motility deep learning swarming inflammatory bowel disease in vitro diagnosis microbiome test |
| title | Deep learning-based detection of bacterial swarm motion using a single image |
| title_full | Deep learning-based detection of bacterial swarm motion using a single image |
| title_fullStr | Deep learning-based detection of bacterial swarm motion using a single image |
| title_full_unstemmed | Deep learning-based detection of bacterial swarm motion using a single image |
| title_short | Deep learning-based detection of bacterial swarm motion using a single image |
| title_sort | deep learning based detection of bacterial swarm motion using a single image |
| topic | Bacterial motility deep learning swarming inflammatory bowel disease in vitro diagnosis microbiome test |
| url | https://www.tandfonline.com/doi/10.1080/19490976.2025.2505115 |
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