ArticleScientific reports2022
Automated recognition of pain in cats.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 64 citations in OpenAlex.
- Artificial intelligence-based automated scoring of the mouse grimace scale: a systematic review.Pain reports · 2026Review
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
- CowPainCheck: AI-based facial expression analysis for dairy cow welfare.Journal of animal science and technology · 2026Review
- Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept.Scientific reports · 2026Article
- Multi-Modal Feature Fusion and Hierarchical Classification for Automated Equine-Human Interaction Behavior Recognition.Sensors (Basel, Switzerland) · 2026Article
- Comparing the performance of deep learning video-based models and trained veterinarians in cattle pain assessment.Scientific reports · 2026Article
- Research Methods for the Analysis of Visual Emotion Cues in Animals: A Workshop Report.Animals : an open access journal from MDPI · 2025Article
- Dog facial landmarks detection and its applications for facial analysis.Scientific reports · 2025Article
- A segment-based framework for explainability in animal affective computing.Scientific reports · 2025Article
- Quantifying Facial Gestures Using Deep Learning in a New World Monkey.American journal of primatology · 2025Article
- Comparison between AI and human expert performance in acute pain assessment in sheep.Scientific reports · 2025Article
- Clinical interpretation of body language and behavioral modifications to recognize pain in domestic mammals.Frontiers in veterinary science · 2025Review
- Examining Mammalian facial behavior using Facial Action Coding Systems (FACS) and combinatorics.PloS one · 2025Article
- Digitalising behavioural data collection through cloud-based technology in veterinary science and beyond.Frontiers in veterinary science · 2025Article
- AI-based prediction and detection of early-onset of digital dermatitis in dairy cows using infrared thermography.Scientific reports · 2024Article
- Automated video-based pain recognition in cats using facial landmarks.Scientific reports · 2024Article
- Computational investigation of the social function of domestic cat facial signals.Scientific reports · 2024Article
- Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.Scientific reports · 2024Article
- AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.