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From facial expressions to algorithms: a narrative review of animal pain recognition technologies.
Chiavaccini, Ludovica; Gupta, Anjali; Chiavaccini, Guido.
Afiliación
  • Chiavaccini L; Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States.
  • Gupta A; Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States.
  • Chiavaccini G; Independent Researcher, Livorno, Italy.
Front Vet Sci ; 11: 1436795, 2024.
Article en En | MEDLINE | ID: mdl-39086767
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Vet Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Vet Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza