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Home / Equine Research Bank / Front Vet Sci / From facial expressions to algorithms: a narrative review of...
Frontiers in veterinary science2024; 11; 1436795; doi: 10.3389/fvets.2024.1436795

From facial expressions to algorithms: a narrative review of animal pain recognition technologies.

Abstract: Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.
Copyright © 2024 Chiavaccini, Gupta and Chiavaccini.
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Publication Date: 2024-07-17 PubMed ID: 39086767PubMed Central: PMC11288915DOI: 10.3389/fvets.2024.1436795Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This review article explores the evolution of technologies used to recognize pain in animals, highlighting the transition from traditional facial expression-based scales to advanced automated pain recognition systems powered by artificial intelligence (AI).
  • It discusses both the benefits and challenges of these emerging AI-driven approaches and underscores their significance for improving animal welfare and veterinary care.

Background on Facial Expression Pain Recognition

  • Facial expressions are a fundamental means of communication and emotional expression not only in humans but across many animal species.
  • Researchers have developed tools like the Horse Grimace Scale (HGS), which rely on humans identifying specific facial features associated with pain in horses.
  • These methods have advanced the ability to detect pain but have inherent limitations including:
    • Subjectivity due to human interpretation, which can vary between observers.
    • Significant training needs for assessors to reliably use these scales.
    • Costs related to time and expertise.
    • Potential biases that may affect accuracy and consistency.
  • Despite these drawbacks, facial expression-based pain scales have been continually refined for different animal species.

Introduction and Potential of Automated Pain Recognition (APR)

  • To overcome the limitations of manual pain recognition, researchers are turning to Automated Pain Recognition (APR) systems powered by Artificial Intelligence (AI).
  • APR employs technologies like computer vision and machine learning to detect and analyze complex pain signals.
  • Advantages include:
    • Objectivity: Algorithms provide consistent and impartial assessments, reducing human bias.
    • Multimodal data integration: APR can analyze not just facial expressions but also body language, vocalizations, and physiological data (e.g., heart rate), giving a more comprehensive pain assessment.
    • Scalability and efficiency: Automated systems can process large volumes of data quickly without fatigue.
  • Application of AI in this context has transformative implications for:
    • Veterinary medicine—enhancing diagnostics and treatment decisions.
    • Animal welfare—improving the recognition and management of pain in diverse animal populations.

Challenges and Limitations of APR

  • While promising, APR faces several key challenges:
    • Data limitations:
      • Requirement for large, high-quality annotated datasets that represent diverse species and pain conditions.
      • Difficulty in obtaining ground truth labels since animal pain cannot be communicated verbally.
    • Ethical considerations:
      • Ensuring that data collection and monitoring respect animal welfare and privacy.
      • Balancing technological intervention without causing additional stress or harm.
    • Technical robustness and generalizability:
      • Models must be validated across various environments, species, and individual differences to be broadly applicable.
      • Potential risk of algorithmic errors or biases if training data is not comprehensive.

Conclusion and Outlook

  • This narrative review traces the journey from the inception of facial expression recognition in animal pain scales to the cutting-edge use of AI in automated pain detection.
  • The integration of AI offers a powerful opportunity to improve the accuracy, speed, and comprehensiveness of pain assessments in animals.
  • Future efforts should focus on addressing data and ethical challenges, developing standardized protocols and ground truth criteria, and fostering interdisciplinary collaboration between veterinarians, computer scientists, and animal welfare experts.
  • Continued progress in this field has the potential to significantly enhance welfare outcomes by ensuring better pain recognition and management in animals who cannot verbally communicate their discomfort.

Cite This Article

APA
Chiavaccini L, Gupta A, Chiavaccini G. (2024). From facial expressions to algorithms: a narrative review of animal pain recognition technologies. Front Vet Sci, 11, 1436795. https://doi.org/10.3389/fvets.2024.1436795

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 11
Pages: 1436795
PII: 1436795

Researcher Affiliations

Chiavaccini, Ludovica
  • Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States.
Gupta, Anjali
  • Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States.
Chiavaccini, Guido
  • Independent Researcher, Livorno, Italy.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Citations

This article has been cited 6 times.
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