Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms.
advanced technologies
behavior
bovine respiratory disease
cattle
early diagnosis
Journal
Animals : an open access journal from MDPI
ISSN: 2076-2615
Titre abrégé: Animals (Basel)
Pays: Switzerland
ID NLM: 101635614
Informations de publication
Date de publication:
30 Sep 2022
30 Sep 2022
Historique:
received:
30
08
2022
revised:
25
09
2022
accepted:
27
09
2022
entrez:
14
10
2022
pubmed:
15
10
2022
medline:
15
10
2022
Statut:
epublish
Résumé
Classically, the diagnosis of respiratory disease in cattle has been based on observation of clinical signs and the behavior of the animals, but this technique can be subjective, time-consuming and labor intensive. It also requires proper training of staff and lacks sensitivity (Se) and specificity (Sp). Furthermore, respiratory disease is diagnosed too late, when the animal already has severe lesions. A total of 104 papers were included in this review. The use of new advanced technologies that allow early diagnosis of diseases using real-time data analysis may be the future of cattle farms. These technologies allow continuous, remote, and objective assessment of animal behavior and diagnosis of bovine respiratory disease with improved Se and Sp. The most commonly used behavioral variables are eating behavior and physical activity. Diagnosis of bovine respiratory disease may experience a significant change with the help of big data combined with machine learning, and may even integrate metabolomics as disease markers. Advanced technologies should not be a substitute for practitioners, farmers or technicians, but could help achieve a much more accurate and earlier diagnosis of respiratory disease and, therefore, reduce the use of antibiotics, increase animal welfare and sustainability of livestock farms. This review aims to familiarize practitioners and farmers with the advantages and disadvantages of the advanced technological diagnostic tools for bovine respiratory disease and introduce recent clinical applications.
Identifiants
pubmed: 36230364
pii: ani12192623
doi: 10.3390/ani12192623
pmc: PMC9558517
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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