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Animal affective computing is an emerging new field, which has so far mainly focused on pain, while other emotional states remain uncharted territories, especially in horses. This study is the first to develop AI models to automatically recognize horse emotional states from facial expressions using data collected in a controlled experiment. We explore two types of pipelines: a deep learning one which takes as input video footage, and a machine learning one which takes as input EquiFACS annotations. The former outperforms the latter, with 76% accuracy in separating between four emotional states: baseline, positive anticipation, disappointment and frustration. Anticipation and frustration were difficult to separate, with only 61% accuracy.

Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases also require special expertise and training. This paper presents the first comparative study of two different paths towards automatizing pain recognition in facial images of domestic short haired cats (n = 29), captured during ovariohysterectomy at different time points corresponding to varying intensities of pain. One approach is based on convolutional neural networks (ResNet50), while the other—on machine learning models based on geometric landmarks analysis inspired by species specific Facial Action Coding Systems (i.e. catFACS). Both types of approaches reach comparable accuracy of above 72%, indicating their potential usefulness as a basis for automating cat pain detection from images.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs’ facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network’s attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye.

Digital dermatitis (DD) is a common foot disease that can cause lameness, decreased milk production and fertility decline in cows. The prediction and early detection of DD can positively impact animal welfare and profitability of the dairy industry. This study applies deep learning-based computer vision techniques for early onset detection and prediction of DD using infrared thermography (IRT) data. We investigated the role of various inputs for these tasks, including thermal images of cow feet, statistical color features extracted from IRT images, and manually registered temperature values. Our models achieved performances of above 81% accuracy on DD detection on ‘day 0’ (first appearance of clinical signs), and above 70% accuracy prediction of DD two days prior to the first appearance of clinical signs. Moreover, current findings indicate that the use of IRT images in conjunction with AI based predictors show real potential for developing future real-time automated tools to monitoring DD in dairy cows.

Manual tools for pain assessment from facial expressions have been suggested and validated for several animal species. However, facial expression analysis performed by humans is prone to subjectivity and bias, and in many cases also requires special expertise and training. This has led to an increasing body of work on automated pain recognition, which has been addressed for several species, including cats. Even for experts, cats are a notoriously challenging species for pain assessment. A previous study compared two approaches to automated ‘pain’/‘no pain’ classification from cat facial images: a deep learning approach, and an approach based on manually annotated geometric landmarks, reaching comparable accuracy results. However, the study included a very homogeneous dataset of cats and thus further research to study generalizability of pain recognition to more realistic settings is required. This study addresses the question of whether AI models can classify ‘pain’/‘no pain’ in cats in a more realistic (multi-breed, multi-sex) setting using a more heterogeneous and thus potentially ‘noisy’ dataset of 84 client-owned cats. Cats were a convenience sample presented to the Department of Small Animal Medicine and Surgery of the University of Veterinary Medicine Hannover and included individuals of different breeds, ages, sex, and with varying medical conditions/medical histories. Cats were scored by veterinary experts using the Glasgow composite measure pain scale in combination with the well-documented and comprehensive clinical history of those patients; the scoring was then used for training AI models using two different approaches. We show that in this context the landmark-based approach performs better, reaching accuracy above 77% in pain detection as opposed to only above 65% reached by the deep learning approach. Furthermore, we investigated the explainability of such machine recognition in terms of identifying facial features that are important for the machine, revealing that the region of nose and mouth seems more important for machine pain classification, while the region of ears is less important, with these findings being consistent across the models and techniques studied here.

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.

This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pain) and after (pain) surgery. Four veterinary experts used two types of pain scoring scales: the sheep facial expression scale (SFPES) and the Unesp-Botucatu composite behavioral scale (USAPS), which is the ‘golden standard’ in sheep pain assessment. The developed AI pipeline based on CLIP encoder significantly outperformed human facial scoring (AUC difference = 0.115, p < 0.001) when having access to the same visual information (front and lateral face images). It further effectively equaled human USAPS behavioral scoring (AUC difference = 0.027, p = 0.163), but the small improvement was not statistically significant. The fact that the machine can outperform human experts in recognizing pain in sheep when exposed to the same visual information has significant implications for clinical practice, which warrant further scientific discussion.

Accurate pain assessment in animals is crucial for ensuring animal welfare and guiding veterinary interventions. Traditional pain evaluation relies on scoring of pain behaviours by veterinarians, which can be influenced by observational variability and individual expertise. There is a growing interest in using AI tools, and the question whether Artificial Intelligence (AI) can outperform humans in animal pain recognition is only beginning to be explored. This study is the first to address cattle pain recognition in this context. Namely, we compare the performance of trained veterinarians in the task of pain recognition in cattle using video-based analysis. Our results show that machine learning models achieve high accuracy in pain classification and demonstrate performance comparable to trained veterinarians, with some advantages in video-based assessments. These findings highlight the potential of machine learning to enhance pain assessment in veterinary medicine, offering a scalable and more objective tool for improving animal welfare.

Affective states are reflected in the facial expressions of all mammals. Facial behaviors linked to pain have attracted most of the attention so far in non-human animals, leading to the development of numerous instruments for evaluating pain through facial expressions for various animal species. Nevertheless, manual facial expression analysis is susceptible to subjectivity and bias, is labor-intensive and often necessitates specialized expertise and training. This challenge has spurred a growing body of research into automated pain recognition, which has been explored for multiple species, including cats. In our previous studies, we have presented and studied artificial intelligence (AI) pipelines for automated pain recognition in cats using 48 facial landmarks grounded in cats’ facial musculature, as well as an automated detector of these landmarks. However, so far automated recognition of pain in cats used solely static information obtained from hand-picked single images of good quality. This study takes a significant step forward in fully automated pain detection applications by presenting an end-to-end AI pipeline that requires no manual efforts in the selection of suitable images or their landmark annotation. By working with video rather than still images, this new pipeline approach also optimises the temporal dimension of visual information capture in a way that is not practical to preform manually. The presented pipeline reaches over 70% and 66% accuracy respectively in two different cat pain datasets, outperforming previous automated landmark-based approaches using single frames under similar conditions, indicating that dynamics matter in cat pain recognition. We further define metrics for measuring different dimensions of deficiencies in datasets with animal pain faces, and investigate their impact on the performance of the presented pain recognition AI pipeline.

Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy. Manually annotating these landmarks, however, is a labor-intensive process, deeming it impractical for generating sufficiently large amounts of data for machine learning purposes and for use in applied real-time contexts with cats. Our previous work introduced an AI pipeline for automated landmark detection, which showed good performance in standard machine learning metrics. Nonetheless, the effectiveness of fully automated, end-to-end landmark-based systems for practical cat facial analysis tasks remained underexplored. In this paper we develop AI pipelines for three benchmark tasks using two previously collected datasets of cat faces. The tasks include automated cat breed recognition, cephalic type recognition and pain recognition. Our fully automated end-to-end pipelines reached accuracy of 75% and 66% in cephalic type and pain recognition respectively, suggesting that landmark-based approaches hold promise for automated pain assessment and morphological explorations.