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Time budgets and weight shifting as indicators of pain in hospitalized horses.
Nowak, Magdalena; Martin-Cirera, Albert; Jenner, Florien; Auer, Ulrike.
Afiliación
  • Nowak M; Anesthesiology and Perioperative Intensive - Care Medicine Unit, Department of Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria.
  • Martin-Cirera A; Precision Livestock Farming Hub and Institute of Animal Husbandry and Animal Welfare, University of Veterinary Medicine Vienna, Vienna, Austria.
  • Jenner F; Equine Surgery Unit, Department of Companion Animals and Horses, University Equine Hospital, University of Veterinary Medicine Vienna, Vienna, Austria.
  • Auer U; Anesthesiology and Perioperative Intensive - Care Medicine Unit, Department of Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria.
Front Pain Res (Lausanne) ; 5: 1410302, 2024.
Article en En | MEDLINE | ID: mdl-39109240
ABSTRACT

Introduction:

Pain assessment in horses presents a significant challenge due to their nonverbal nature and their tendency to conceal signs of discomfort in the presence of potential threats, including humans. Therefore, this study aimed to identify pain-associated behaviors amenable to automated AI-based detection in video recordings. Additionally, it sought to determine correlations between pain intensity and behavioral and postural parameters by analyzing factors such as time budgets, weight shifting, and unstable resting. The ultimate goal is to facilitate the development of AI-based quantitative tools for pain assessment in horses. Materials and

methods:

A cohort of 20 horses (mean age 15 ± 8) admitted to a university equine hospital underwent 24-h video recording. Behaviors were manually scored and retrospectively analyzed using Loopy® software. Three pain groups were established based on the Pain Score Vetmeduni Vienna pain-free (P0), mild to moderate pain (P1), and severe pain (P2).

Results:

Weight shifting emerged as a reliable indicator for discriminating between painful and pain-free horses, with significant differences observed between pain groups (p < 0.001) and before and after administration of analgesia. Additionally, severely painful horses (P2 group) exhibited lower frequencies of feeding and resting standing per hour compared to pain-free horses, while displaying a higher frequency of unstable resting per hour.

Discussion:

The significant differences observed in these parameters between pain groups offer promising prospects for AI-based analysis and automated pain assessment in equine medicine. Further investigation is imperative to establish precise thresholds. Leveraging such technology has the potential to enable more effective pain detection and management in horses, ultimately enhancing welfare and informing clinical decision-making in equine medicine.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Pain Res (Lausanne) Año: 2024 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Pain Res (Lausanne) Año: 2024 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Suiza