ObjectiveThe objective of this study was to investigate the existence of an equine pain face and to describe this in detail.Study designSemi-randomized, controlled, crossover trial.AnimalsSix adult horses.MethodsPain was induced with two noxious stimuli, a tourniquet on the antebrachium and topical application of capsaicin. All horses participated in two control trials and received both noxious stimuli twice, once with and once without an observer present. During all sessions their pain state was scored. The horses were filmed and the close-up video recordings of the faces were analysed for alterations in behaviour and facial expressions. Still images from the trials were evaluated for the presence of each of the specific pain face features identified from the video analysis.ResultsBoth noxious challenges were effective in producing a pain response resulting in significantly increased pain scores. Alterations in facial expressions were observed in all horses during all noxious stimulations. The number of pain face features present on the still images from the noxious challenges were significantly higher than for the control trial (p = 0.0001). Facial expressions representative for control and pain trials were condensed into explanatory illustrations. During pain sessions with an observer present, the horses increased their contact-seeking behavior.Conclusions and clinical relevanceAn equine pain face comprising ‘low’ and/or ‘asymmetrical’ ears, an angled appearance of the eyes, a withdrawn and/or tense stare, mediolaterally dilated nostrils and tension of the lips, chin and certain facial muscles can be recognized in horses during induced acute pain. This description of an equine pain face may be useful for improving tools for pain recognition in horses with mild to moderate pain.

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Summary Pain management is dependent on the quality of the pain evaluation. Ideally, pain evaluation is objective, pain‐specific and easily incorporated into a busy equine clinic. This paper reviews the existing knowledge base regarding the identification and quantification of pain in horses. Behavioural indicators of pain in horses in the context of normal equine behaviour, as well as various physiological parameters potentially useful for pain evaluation, are discussed. Areas where knowledge is sparse are identified and a new equine pain scale based on results from all reviewed papers is proposed. Finally, the most important considerations in relation to the implementation of a pain scale in a hospital setting are discussed.

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.

During the last decade, a number of pain assessment tools based on facial expressions have been developed for horses. While all tools focus on moveable facial muscles related to the ears, eyes, nostrils, lips, and chin, results are difficult to compare due to differences in the research conditions, descriptions and methodologies. We used a Facial Action Coding System (FACS) modified for horses (EquiFACS) to code and analyse video recordings of acute short-term experimental pain (n = 6) and clinical cases expected to be in pain or without pain (n = 21). Statistical methods for analyses were a frequency based method adapted from human FACS approaches, and a novel method based on co-occurrence of facial actions in time slots of varying lengths. We describe for the first time changes in facial expressions using EquiFACS in video of horses with pain. The ear rotator (EAD104), nostril dilation (AD38) and lower face behaviours, particularly chin raiser (AU17), were found to be important pain indicators. The inner brow raiser (AU101) and eye white increase (AD1) had less consistent results across experimental and clinical data. Frequency statistics identified AUs, EADs and ADs that corresponded well to anatomical regions and facial expressions identified by previous horse pain research. The co-occurrence based method additionally identified lower face behaviors that were pain specific, but not frequent, and showed better generalization between experimental and clinical data. In particular, chewing (AD81) was found to be indicative of pain. Lastly, we identified increased frequency of half blink (AU47) as a new indicator of pain in the horses of this study.