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

Lookup NU author(s): Dr Matthew Leach

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2018 Dalla Costa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.

Publication metadata

Author(s): Costa ED, Pascuzzo R, Leach MC, Dai F, Lebelt D, Vantini S, Minero M

Publication type: Article

Publication status: Published

Journal: PLoS ONE

Year: 2018

Volume: 13

Issue: 8

Online publication date: 01/08/2018

Acceptance date: 25/06/2018

Date deposited: 13/08/2018

ISSN (electronic): 1932-6203

Publisher: Public Library of Science

URL: https://doi.org/10.1371/journal.pone.0200339

DOI: 10.1371/journal.pone.0200339

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