Early detection of infectious bovine keratoconjunctivitis with artificial intelligence.

Artificial intelligence animal welfare biosecurity cattle cattle disease deep learning infectious bovine keratoconjunctivitis muzzles neural networks pinkeye

Journal

Veterinary research
ISSN: 1297-9716
Titre abrégé: Vet Res
Pays: England
ID NLM: 9309551

Informations de publication

Date de publication:
15 Dec 2023
Historique:
received: 11 09 2023
accepted: 11 11 2023
medline: 16 12 2023
pubmed: 16 12 2023
entrez: 15 12 2023
Statut: epublish

Résumé

Artificial intelligence (AI) was developed to distinguish cattle by their muzzle patterns and identify early cases of disease, including infectious bovine keratoconjunctivitis (IBK). It was tested on 870 cattle in four locations, with 170 developing IBK. The AI identified 169 of the 170 cases prior to their identification by veterinarians, and another 17 cases that remained free of IBK signs (sensitivity = 99.4%, specificity = 97.6%). These results indicate the AI can detect emerging IBK cases by muzzle images very early in the disease process and be used as an intervention tool in the prevention of IBK outbreaks.

Identifiants

DOI: 10.1186/s13567-023-01255-w PMID: 38102629
pubmed: 38102629
doi: 10.1186/s13567-023-01255-w
pii: 10.1186/s13567-023-01255-w
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

122

Informations de copyright

© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Références

Dennis EJ, Kneipp M (2021) A review of global prevalence and economic impacts of infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:355–369. https://doi.org/10.1016/j.cvfa.2021.03.010
doi: 10.1016/j.cvfa.2021.03.010 pubmed: 34049665
Irby NL, Angelos JA (2018) 14 - Ocular Diseases, from Rebhun’s Diseases of Dairy Cattle. 3
Kneipp M (2021) Defining and diagnosing infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:237–252. https://doi.org/10.1016/j.cvfa.2021.03.001
doi: 10.1016/j.cvfa.2021.03.001 pubmed: 34049656
Dewell RD, Millman ST, Gould SA, Tofflemire KL, Whitley RD, Parsons RL, Rowe EW, Liu F, Wang C, O’Connor AM (2014) Evaluating approaches to measuring ocular pain in bovine calves with corneal scarification and infectious bovine keratoconjunctivitis-associated corneal ulcerations. J Anim Sci 92:1161–1172. https://doi.org/10.2527/jas.2013-7264
doi: 10.2527/jas.2013-7264 pubmed: 24504043
Dickey AM, Loy JD, Bono JL, Smith TP, Apley MD, Lubbers BV, DeDonder KD, Capik SF, Larson RL, White BJ, Blom J, Chitko-McKown CG, Clawson ML (2016) Large genomic differences between Moraxella bovoculi isolates acquired from the eyes of cattle with infectious bovine keratoconjunctivitis versus the deep nasopharynx of asymptomatic cattle. Vet Res 47:31. https://doi.org/10.1186/s13567-016-0316-2
doi: 10.1186/s13567-016-0316-2 pubmed: 26872821 pmcid: 4752781
Dickey AM, Schuller G, Loy JD, Clawson ML (2018) Whole genome sequencing of Moraxella bovoculi reveals high genetic diversity and evidence for interspecies recombination at multiple loci. PLoS One 13:e0209113. https://doi.org/10.1371/journal.pone.0209113
doi: 10.1371/journal.pone.0209113 pubmed: 30557405 pmcid: 6296526
Loy JD, Clothier KA, Maier G (2021) Component causes of infectious bovine keratoconjunctivitis-non-Moraxella organisms in the epidemiology of infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:295–308. https://doi.org/10.1016/j.cvfa.2021.03.005
doi: 10.1016/j.cvfa.2021.03.005 pubmed: 34049660
O’Connor AM, Kneipp M (2021) Evidence base for treatment of infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:329–339. https://doi.org/10.1016/j.cvfa.2021.03.008
doi: 10.1016/j.cvfa.2021.03.008 pubmed: 34049663
Velazquez-Meza ME, Galarde-Lopez M, Carrillo-Quiroz B, Alpuche-Aranda CM (2022) Antimicrobial resistance: one health approach. Vet World 15:743–749. https://doi.org/10.14202/vetworld.2022.743-749
doi: 10.14202/vetworld.2022.743-749 pubmed: 35497962 pmcid: 9047147
Sagar P, Aseem A, Banjara SK, Veleri S (2023) The role of food chain in antimicrobial resistance spread and one health approach to reduce risks. Int J Food Microbiol 391–393:110148. https://doi.org/10.1016/j.ijfoodmicro.2023.110148
doi: 10.1016/j.ijfoodmicro.2023.110148 pubmed: 36868045
Maier G, O’Connor AM, Sheedy D (2021) The evidence base for prevention of infectious bovine keratoconjunctivitis through vaccination. Vet Clin North Am Food Anim Pract 37:341–353. https://doi.org/10.1016/j.cvfa.2021.03.009
doi: 10.1016/j.cvfa.2021.03.009 pubmed: 34049664
Hille MM, Spangler ML, Clawson ML, Heath KD, Vu HLX, Rogers RES, Loy JD (2022) A five year randomized controlled trial to assess the efficacy and antibody responses to a commercial and autogenous vaccine for the prevention of infectious bovine keratoconjunctivitis. Vaccines 10:916. https://doi.org/10.3390/vaccines10060916
doi: 10.3390/vaccines10060916 pubmed: 35746524 pmcid: 9228096
Petersen WE (1922) The identification of the bovine by means of nose-prints. J Dairy Sci 5:249–258
doi: 10.3168/jds.S0022-0302(22)94150-5
Tharwat A, Gaber T, Hassanien AE, Hassanien HA (2014) Cattle identification using muzzle print images based on texture features approach. In: Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA. Advances in Intelligent Systems and Computing vol. 303. Czech Republic
Awad AI, Hassaballah M (2019) Bag-of-visual-words for cattle identification from muzzle print images. Appl Sci 9:4914. https://doi.org/10.3390/app9224914
doi: 10.3390/app9224914
Hossain ME, Kabir MA, Zheng L, Swain DL, McGrath S, Medway J (2022) A systematic review of machine learning techniques for cattle identification: datasets, methods and future directions. Artif Intell Agric 6:138–155
Steele L, Orefuwa E, Bino S, Singer SR, Lutwama J, Dickmann P (2020) Earlier outbreak detection-A generic model and novel methodology to guide earlier detection supported by data from low- and mid-income countries. Front Public Health 8:452. https://doi.org/10.3389/fpubh.2020.00452
doi: 10.3389/fpubh.2020.00452 pubmed: 33014967 pmcid: 7516212
Maier G, Doan B, O’Connor AM (2021) The role of environmental factors in the epidemiology of infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:309–320. https://doi.org/10.1016/j.cvfa.2021.03.006
doi: 10.1016/j.cvfa.2021.03.006 pubmed: 34049661
Angelos JA (2015) Infectious bovine keratoconjunctivitis (pinkeye). Vet Clin North Am Food Anim Pract 31(61–79):v–vi. https://doi.org/10.1016/j.cvfa.2014.11.006
doi: 10.1016/j.cvfa.2014.11.006
Wynn EL, Hille MM, Loy JD, Schuller G, Kuhn KL, Dickey AM, Bono JL, Clawson ML (2022) Whole genome sequencing of Moraxella bovis strains from North America reveals two genotypes with different genetic determinants. BMC Microbiol 22:258. https://doi.org/10.1186/s12866-022-02670-3
doi: 10.1186/s12866-022-02670-3 pubmed: 36271336 pmcid: 9585708
Loy JD, Hille M, Maier G, Clawson ML (2021) Component causes of infectious bovine keratoconjunctivitis—the role of Moraxella species in the epidemiology of infectious bovine keratoconjunctivitis. Vet Clin North Am Food Anim Pract 37:279–293. https://doi.org/10.1016/j.cvfa.2021.03.004
doi: 10.1016/j.cvfa.2021.03.004 pubmed: 34049659

Auteurs

Shekhar Gupta (S)

MyAnIML, Overland Park, KS, 66223, USA. shekhar@myaniml.com.

Larry A Kuehn (LA)

United States Department of Agriculture (USDA), Agricultural Research Service (ARS), U. S. Meat Animal Research Center, Clay Center, NE, 68933, USA.

Michael L Clawson (ML)

United States Department of Agriculture (USDA), Agricultural Research Service (ARS), U. S. Meat Animal Research Center, Clay Center, NE, 68933, USA. mike.clawson@usda.gov.
ORCID: 0000-0002-3355-5390

Classifications MeSH