Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human obser...

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Autores principales:	Gabriel Carreira Lencioni, Rafael Vieira de Sousa, Edson José de Souza Sardinha, Rodrigo Romero Corrêa, Adroaldo José Zanella
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4c07ec742ee14cae9b2ad3a7f908ad23
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id	oai:doaj.org-article:4c07ec742ee14cae9b2ad3a7f908ad23
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spelling	oai:doaj.org-article:4c07ec742ee14cae9b2ad3a7f908ad232021-12-02T20:16:47ZPain assessment in horses using automatic facial expression recognition through deep learning-based modeling.1932-620310.1371/journal.pone.0258672https://doaj.org/article/4c07ec742ee14cae9b2ad3a7f908ad232021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258672https://doaj.org/toc/1932-6203The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.Gabriel Carreira LencioniRafael Vieira de SousaEdson José de Souza SardinhaRodrigo Romero CorrêaAdroaldo José ZanellaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258672 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gabriel Carreira Lencioni Rafael Vieira de Sousa Edson José de Souza Sardinha Rodrigo Romero Corrêa Adroaldo José Zanella Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
description	The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.
format	article
author	Gabriel Carreira Lencioni Rafael Vieira de Sousa Edson José de Souza Sardinha Rodrigo Romero Corrêa Adroaldo José Zanella
author_facet	Gabriel Carreira Lencioni Rafael Vieira de Sousa Edson José de Souza Sardinha Rodrigo Romero Corrêa Adroaldo José Zanella
author_sort	Gabriel Carreira Lencioni
title	Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
title_short	Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
title_full	Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
title_fullStr	Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
title_full_unstemmed	Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
title_sort	pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4c07ec742ee14cae9b2ad3a7f908ad23
work_keys_str_mv	AT gabrielcarreiralencioni painassessmentinhorsesusingautomaticfacialexpressionrecognitionthroughdeeplearningbasedmodeling AT rafaelvieiradesousa painassessmentinhorsesusingautomaticfacialexpressionrecognitionthroughdeeplearningbasedmodeling AT edsonjosedesouzasardinha painassessmentinhorsesusingautomaticfacialexpressionrecognitionthroughdeeplearningbasedmodeling AT rodrigoromerocorrea painassessmentinhorsesusingautomaticfacialexpressionrecognitionthroughdeeplearningbasedmodeling AT adroaldojosezanella painassessmentinhorsesusingautomaticfacialexpressionrecognitionthroughdeeplearningbasedmodeling
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Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.

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