Deep learning from videography as a tool for measuring E. coli infection in poultry
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| Publicat a: | bioRxiv (Jan 21, 2025) |
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| Autor principal: | |
| Altres autors: | , , , , , , , , |
| Publicat: |
Cold Spring Harbor Laboratory Press
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| Accés en línia: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 001 | 3131626246 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2024.11.20.624075 |2 doi | |
| 035 | |a 3131626246 | ||
| 045 | 0 | |b d20250121 | |
| 100 | 1 | |a Scheidwasser, Neil | |
| 245 | 1 | |a Deep learning from videography as a tool for measuring E. coli infection in poultry | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Jan 21, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Poultry farming is threatened by regular outbreaks of Escherichia coli (E. coli) that lead to significant economic losses and public health risks. However, traditional surveillance methods often lack sensitivity and scalability. Early detection of infected poultry using minimally invasive procedures is thus essential for preventing epidemics. To that end, we leverage recent advancements in computer vision, employing deep learning-based tracking to detect behavioural changes associated with E. coli infection in a case-control trial comprising two groups of 20 broiler chickens: (1) a healthy control group and (2) a group infected with a pathogenic E. coli field strain from the poultry industry. More specifically, kinematic features derived from deep learning-based tracking data revealed markedly reduced activity in the challenged group compared to the negative control. These findings were validated by lower mean optical flow in the infected flock, suggesting reduced movement and activity, and post-mortem physiological markers of inflammation which confirmed the severity of infection in the challenged group. Overall, this study demonstrates that deep learning-based tracking offers a promising solution for real-time monitoring and early infection detection in poultry farming, with the potential to help reduce economic losses and mitigate public health risks associated with infectious disease outbreaks in poultry.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Author affiliations updated; added new validation based on optical flow; minor changes throughout the manuscript* https://github.com/Neclow/dlc4ecoli | |
| 653 | |a Infections | ||
| 653 | |a Amyloid | ||
| 653 | |a Food sources | ||
| 653 | |a Public health | ||
| 653 | |a Deep learning | ||
| 653 | |a E coli | ||
| 653 | |a Vaccination | ||
| 653 | |a Poultry | ||
| 653 | |a Poultry farming | ||
| 653 | |a Epidemics | ||
| 653 | |a Animal welfare | ||
| 653 | |a Escherichia coli | ||
| 700 | 1 | |a Louise Ladefoged Poulsen | |
| 700 | 1 | |a Prince Ravi Leow | |
| 700 | 1 | |a Mark Poulsen Khurana | |
| 700 | 1 | |a Iglesias-Carrasco, Maider | |
| 700 | 1 | |a Laydon, Daniel Joseph | |
| 700 | 1 | |a Donnelly, Christl Ann | |
| 700 | 1 | |a Anders Miki Bojesen | |
| 700 | 1 | |a Bhatt, Samir | |
| 700 | 1 | |a Duchene, David A | |
| 773 | 0 | |t bioRxiv |g (Jan 21, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3131626246/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3131626246/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2024.11.20.624075v2 |