Nowak Magdalena, Martin-Cirera Albert, Jenner Florien, Auer Ulrike
Anesthesiology and Perioperative Intensive - Care Medicine Unit, Department of Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria.
Precision Livestock Farming Hub and Institute of Animal Husbandry and Animal Welfare, University of Veterinary Medicine Vienna, Vienna, Austria.
Front Pain Res (Lausanne). 2024 Jul 23;5:1410302. doi: 10.3389/fpain.2024.1410302. eCollection 2024.
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.
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).
Weight shifting emerged as a reliable indicator for discriminating between painful and pain-free horses, with significant differences observed between pain groups ( < 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.
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.
由于马不会说话，且在包括人类在内的潜在威胁面前倾向于隐藏不适迹象，因此对马进行疼痛评估面临重大挑战。因此，本研究旨在识别视频记录中适合基于人工智能自动检测的疼痛相关行为。此外，它试图通过分析时间分配、体重转移和不稳定休息等因素，确定疼痛强度与行为和姿势参数之间的相关性。最终目标是促进开发基于人工智能的马疼痛评估定量工具。
一组20匹马（平均年龄15±8岁）入住大学马医院，进行了24小时视频记录。使用Loopy®软件对手动评分的行为进行回顾性分析。根据维也纳兽医大学疼痛评分建立了三个疼痛组：无痛（P0）、轻至中度疼痛（P1）和重度疼痛（P2）。
体重转移成为区分疼痛马和无痛马的可靠指标，疼痛组之间（<0.001）以及镇痛前后均观察到显著差异。此外，与无痛马相比，重度疼痛马（P2组）每小时进食和站立休息的频率较低，而每小时不稳定休息的频率较高。
疼痛组之间在这些参数上观察到的显著差异为马医学中基于人工智能的分析和自动疼痛评估提供了有希望的前景。必须进行进一步调查以确定精确的阈值。利用此类技术有可能在马中实现更有效的疼痛检测和管理，最终提高福利并为马医学的临床决策提供依据。
