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Czupryna AM, Estepho M, Lugelo A, Bigambo M, Sambo M, Changalucha J, Lushasi KS, Rooyakkers P, Hampson K, Lankester F. Testing novel facial recognition technology to identify dogs during vaccination campaigns. Sci Rep 2023; 13:22025. [PMID: 38086911 PMCID: PMC10716125 DOI: 10.1038/s41598-023-49522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
A lack of methods to identify individual animals can be a barrier to zoonoses control. We developed and field-tested facial recognition technology for a mobile phone application to identify dogs, which we used to assess vaccination coverage against rabies in rural Tanzania. Dogs were vaccinated, registered using the application, and microchipped. During subsequent household visits to validate vaccination, dogs were registered using the application and their vaccination status determined by operators using the application to classify dogs as vaccinated (matched) or unvaccinated (unmatched), with microchips validating classifications. From 534 classified dogs (251 vaccinated, 283 unvaccinated), the application specificity was 98.9% and sensitivity 76.2%, with positive and negative predictive values of 98.4% and 82.8% respectively. The facial recognition algorithm correctly matched 249 (99.2%) vaccinated and microchipped dogs (true positives) and failed to match two (0.8%) vaccinated dogs (false negatives). Operators correctly identified 186 (74.1%) vaccinated dogs (true positives), and 280 (98.9%) unvaccinated dogs (true negatives), but incorrectly classified 58 (23.1%) vaccinated dogs as unmatched (false negatives). Reduced application sensitivity resulted from poor quality photos and light-associated color distortion. With development and operator training, this technology has potential to be a useful tool to identify dogs and support research and intervention programs.
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Affiliation(s)
- Anna Maria Czupryna
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania
| | - Mike Estepho
- PiP My Pet Technologies, Vancouver, British Colombia, Canada
| | - Ahmed Lugelo
- Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania
- Global Animal Health Tanzania, P.O. Box 1642, Arusha, Tanzania
- Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, P.O. Box 3105, Morogoro, Tanzania
| | | | - Maganga Sambo
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania
| | - Joel Changalucha
- Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania
- Global Animal Health Tanzania, P.O. Box 1642, Arusha, Tanzania
| | - Kennedy Selestin Lushasi
- Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania
- Department of Global Health and Biomedical Sciences, Nelson Mandela African Institute of Science and Technology, Arusha, Tanzania
| | | | - Katie Hampson
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Felix Lankester
- Global Animal Health Tanzania, P.O. Box 1642, Arusha, Tanzania.
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, 99164, USA.
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