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Conceptual and methodological issues relating to pain assessment in mammals: the development and utilisation of pain facial expression scales.Effective management of pain is critical to the improvement of animal welfare. For this to happen, pain must be recognised and assessed in a variety of contexts. Pain is a complex phenomenon, making reliable, valid, and feasible measurement challenging. The use of facial expressions as a technique to assess pain in non-verbal human patients has been widely utilised for many years. More recently this technique has been developed for use in a number of non-human species: rodents, rabbits, ferrets, cats, sheep, pigs and horses. Facial expression scoring has been demonstrated to provide an effective means of identifying animal pain and in assessing its severity, overcoming some of the limitations of other measures for pain assessment in animals. However, there remain limitations and challenges to the use of facial expression as a welfare assessment tool which must be investigated. This paper reviews current facial expression pain scales (“Grimace Scales"), discussing the general conceptual and methodological issues faced when assessing pain, and highlighting the advantages of using facial expression scales over other pain assessment methods. We provide guidance on how facial expression scales should be developed so as to be valid and reliable, but we also provide guidance on how they should be used in clinical practice.
The development of a facial expression scale using footrot and mastitis as models of pain in sheepManagement of pain in sheep is limited by the challenges of recognising and accurately quantifying 35 pain in this species. The use of facial expression scoring to assess pain is a well-utilised, practical tool 36 in both humans and non-human animals. The objective of this study was to develop a standardised 37 facial expression pain scale for adult sheep, that could be used reliably and accurately to detect pain 38 associated with naturally occurring painful diseases, such as footrot and mastitis. We also investigated 39 whether the scale could be reliably and accurately utilised by observers after training, enabling the 40 development of an on-farm pain assessment tool. The Sheep Pain Facial Expression Scale (SPFES) 41 was able to correctly identify sheep suffering from disease with a high degree of accuracy (AUC; 42 Footrot: 0.81, Mastitis: 0.80). Diseased sheep scored higher on the scale than controls on the day of 43 disease identification (P<0.05) and diseased sheep showed changes in their facial expression after 44 treatment (P<0.001). The abnormal facial expressions of diseased sheep reduced over time, and at 45 recovery were in line with control sheep. Control sheep did not change their facial expression over 46 time. Five scorers who were trained to use the developed scale also assessed the facial expressions of 47 sheep. The scorers were blind to treatment and session. Scorers reliably (ICC: 0.86) and accurately (α 48 = 0.86) identified changes in the facial expression of sheep with footrot over time (P<0.05), and 49 scored control sheep consistently low over time. The SPFES offers a reliable and effective method of 50 assessing pain in sheep after minimal training.
Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries).Detecting signs of pain in sheep is a challenging problem, as they are a prey species and would usually try to hide any signs that they are unwell or injured. This means that treating ill or injured sheep and preventing any further spread of contagious diseases such as footrot can be slow. The recent development and publication of a Sheep Pain Facial Expression Scale (SPFES) has provided a tool to reliably detect pain in this species. However, due to the increase in intensification in farming and larger flock sizes being cared for by individual farmers, there is less time to spend monitoring sheep for changes in behaviour that may indicate illness or injury. Having an automated system that could detect changes in the facial expression of individual sheep would mean that farmers could receive information directly about particular individuals that need assessment. This would allow treatment to be provided in a timely and direct manner, reducing suffering. We have been developing the SPFES further in order for it to become an automated system. In this paper, we present our novel framework that integrates SPFES concepts with automatic facial expression analysis technologies.