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PloS one2018; 13(8); e0200339; doi: 10.1371/journal.pone.0200339

Can grimace scales estimate the pain status in horses and mice? A statistical approach to identify a classifier.

Abstract: Pain recognition is fundamental for safeguarding animal welfare. Facial expressions have been investigated in several species and grimace scales have been developed as pain assessment tool in many species including horses (HGS) and mice (MGS). This study is intended to progress the validation of grimace scales, by proposing a statistical approach to identify a classifier that can estimate the pain status of the animal based on Facial Action Units (FAUs) included in HGS and MGS. To achieve this aim, through a validity study, the relation between FAUs included in HGS and MGS and the real pain condition was investigated. A specific statistical approach (Cumulative Link Mixed Model, Inter-rater reliability, Multiple Correspondence Analysis, Linear Discriminant Analysis and Support Vector Machines) was applied to two datasets. Our results confirm the reliability of both scales and show that individual FAU scores of HGS and MGS are related to the pain state of the animal. Finally, we identified the optimal weights of the FAU scores that can be used to best classify animals in pain with an accuracy greater than 70%. For the first time, this study describes a statistical approach to develop a classifier, based on HGS and MGS, for estimating the pain status of animals. The classifier proposed is the starting point to develop a computer-based image analysis for the automatic recognition of pain in horses and mice.
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Publication Date: 2018-08-01 PubMed ID: 30067759PubMed Central: PMC6070187DOI: 10.1371/journal.pone.0200339Google 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.

This research study aims to advance the validation of grimace scales, commonly used for pain assessment in animals like horses and mice, and uses statistical methods to identify a classifier that predicts an animal’s pain status based on its facial expressions. The study’s findings confirm the grimace scale’s reliability and propose a classifier with over 70% accuracy for recognizing pain in horses and mice.

Research Overview

  • The research aims to contribute to the validation of grimace scales, which are scales that use facial expressions to assess pain in animals, particularly horses and mice.
  • The researchers apply several advanced statistical methods to develop a classifier—a rule or algorithm that helps identify the pain state of an animal based on specific facial movements.

Methods and Approach

  • The researchers studied the relationship between Facial Action Units (FAUs), which are distinct facial movements coded in the grimace scales, and actual pain conditions in the animals.
  • They applied complex statistical methods–Cumulative Link Mixed Model, Inter-rater reliability, Multiple Correspondence Analysis, Linear Discriminant Analysis, and Support Vector Machines–to two distinct datasets for their analysis.

Results

  • The results validate the reliability of both the horse and mouse grimace scales, confirming that individual scores on each FAU correlate with an animal’s pain state.
  • The researchers then identified optimal weights for each FAU score, enabling them to classify animals as in pain with an accuracy rate above 70%.

Implications and Future Work

  • As a significant development in pain recognition in animals, this research proposes the first statistically sound approach for developing a classifier using horser and mouse grimace scales.
  • This classifier serves as a foundation for future work on developing a computer-based image analysis system for automatically recognizing pain in horses and mice, which could significantly enhance animal welfare through better pain management.

Cite This Article

APA
Dalla Costa E, Pascuzzo R, Leach MC, Dai F, Lebelt D, Vantini S, Minero M. (2018). Can grimace scales estimate the pain status in horses and mice? A statistical approach to identify a classifier. PLoS One, 13(8), e0200339. https://doi.org/10.1371/journal.pone.0200339

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 13
Issue: 8
Pages: e0200339

Researcher Affiliations

Dalla Costa, Emanuela
  • Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, Milan, Italy.
Pascuzzo, Riccardo
  • MOX Laboratory for Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy.
Leach, Matthew C
  • Newcastle University, School of Natural and Environmental Sciences, Newcastle upon Tyne, United Kingdom.
Dai, Francesca
  • Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, Milan, Italy.
Lebelt, Dirk
  • Equine Research & Consulting, Inca, Spain.
Vantini, Simone
  • MOX Laboratory for Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy.
Minero, Michela
  • Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, Milan, Italy.

MeSH Terms

  • Anesthesia, General
  • Anesthetics, Local / administration & dosage
  • Animals
  • Bupivacaine / administration & dosage
  • Discriminant Analysis
  • Ear / physiology
  • Facial Expression
  • Horses
  • Male
  • Mice
  • Nose / physiology
  • Pain / pathology
  • Pain Measurement / methods
  • Support Vector Machine
  • Vasectomy

Grant Funding

  • Wellcome Trust
  • G1100563/1 / National Centre for the Replacement, Refinement and Reduction of Animals in Research
  • Biotechnology and Biological Sciences Research Council

Conflict of Interest Statement

Dirk Lebelt is not employed by a commercial company. \"Equine Research & Consulting\" is the name under which he works as an independent researcher and science consultant. He did not receive any funding other the ones declared in the original Funding Statement nor did he act as a funder for this study. \"Equine Research & Consulting\" is a commercial affiliation, that does not alter our adherence to PLOS ONE policies on sharing data and materials.

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