Deep learning for video-based automated pain recognition in rabbits.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 09 2023
06 09 2023
Historique:
received:
11
06
2023
accepted:
31
08
2023
medline:
8
9
2023
pubmed:
7
9
2023
entrez:
6
9
2023
Statut:
epublish
Résumé
Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data.
Identifiants
pubmed: 37674052
doi: 10.1038/s41598-023-41774-2
pii: 10.1038/s41598-023-41774-2
pmc: PMC10482887
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14679Informations de copyright
© 2023. Springer Nature Limited.
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