Can grimace scales estimate the pain status in horses and mice? A statistical approach to identify a classifier
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 gri...
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| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0200339 |
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A statistical approach to identify a classifier</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Dalla Costa, Emanuela ; Pascuzzo, Riccardo ; Leach, Matthew C ; Dai, Francesca ; Lebelt, Dirk ; Vantini, Simone ; Minero, Michela</creator><creatorcontrib>Dalla Costa, Emanuela ; Pascuzzo, Riccardo ; Leach, Matthew C ; Dai, Francesca ; Lebelt, Dirk ; Vantini, Simone ; Minero, Michela</creatorcontrib><description>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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0200339</identifier><identifier>PMID: 30067759</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Anesthesia, General ; Anesthetics, Local - administration & dosage ; Animal welfare ; Animals ; Biology and Life Sciences ; Bupivacaine - administration & dosage ; Classification ; Classifiers ; Discriminant Analysis ; Ear - physiology ; Facial Expression ; Horses ; Image analysis ; Image processing ; Laboratories ; Male ; Medicine and Health Sciences ; Methods ; Mice ; Nose - physiology ; Object recognition ; Ostomy ; Pain ; Pain - pathology ; Pain management ; Pain Measurement - methods ; Physical Sciences ; Physiology ; Reliability analysis ; Research and Analysis Methods ; Statistics ; Support Vector Machine ; Support vector machines ; Validity ; Vasectomy</subject><ispartof>PloS one, 2018-08, Vol.13 (8), p.e0200339</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Dalla Costa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Dalla Costa et al 2018 Dalla Costa et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-4a56af962c68ea2ca74ab8ad32dfa96660e73c4a317b7bfa2e7e4c71679ff7003</citedby><cites>FETCH-LOGICAL-c758t-4a56af962c68ea2ca74ab8ad32dfa96660e73c4a317b7bfa2e7e4c71679ff7003</cites><orcidid>0000-0001-6555-1784 ; 0000-0001-8255-5306 ; 0000-0002-5037-3759</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6070187/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6070187/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30067759$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dalla Costa, Emanuela</creatorcontrib><creatorcontrib>Pascuzzo, Riccardo</creatorcontrib><creatorcontrib>Leach, Matthew C</creatorcontrib><creatorcontrib>Dai, Francesca</creatorcontrib><creatorcontrib>Lebelt, Dirk</creatorcontrib><creatorcontrib>Vantini, Simone</creatorcontrib><creatorcontrib>Minero, Michela</creatorcontrib><title>Can grimace scales estimate the pain status in horses and mice? 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30067759</pmid><doi>10.1371/journal.pone.0200339</doi><orcidid>https://orcid.org/0000-0001-6555-1784</orcidid><orcidid>https://orcid.org/0000-0001-8255-5306</orcidid><orcidid>https://orcid.org/0000-0002-5037-3759</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Anesthesia, General Anesthetics, Local - administration & dosage Animal welfare Animals Biology and Life Sciences Bupivacaine - administration & dosage Classification Classifiers Discriminant Analysis Ear - physiology Facial Expression Horses Image analysis Image processing Laboratories Male Medicine and Health Sciences Methods Mice Nose - physiology Object recognition Ostomy Pain Pain - pathology Pain management Pain Measurement - methods Physical Sciences Physiology Reliability analysis Research and Analysis Methods Statistics Support Vector Machine Support vector machines Validity Vasectomy |
| title | Can grimace scales estimate the pain status in horses and mice? A statistical approach to identify a classifier |
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