An automated rat grimace scale for the assessment of pain
Abstract Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action un...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46123-x |
| _version_ | 1797637010454740992 |
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| author | Brendan Arnold Rahul Ramakrishnan Amirah Wright Kelsey Wilson Pamela J. VandeVord |
| author_facet | Brendan Arnold Rahul Ramakrishnan Amirah Wright Kelsey Wilson Pamela J. VandeVord |
| author_sort | Brendan Arnold |
| collection | DOAJ |
| description | Abstract Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81–93%. The system’s performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS. |
| first_indexed | 2024-03-11T12:42:14Z |
| format | Article |
| id | doaj.art-e5a92bf928e446839bd6da8efb118a30 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T12:42:14Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e5a92bf928e446839bd6da8efb118a302023-11-05T12:14:37ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-46123-xAn automated rat grimace scale for the assessment of painBrendan Arnold0Rahul Ramakrishnan1Amirah Wright2Kelsey Wilson3Pamela J. VandeVord4School of Biomedical Engineering and Sciences, Virginia TechAcademy of Data Science, Virginia TechSchool of Biomedical Engineering and Sciences, Virginia TechSchool of Biomedical Engineering and Sciences, Virginia TechSchool of Biomedical Engineering and Sciences, Virginia TechAbstract Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81–93%. The system’s performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS.https://doi.org/10.1038/s41598-023-46123-x |
| spellingShingle | Brendan Arnold Rahul Ramakrishnan Amirah Wright Kelsey Wilson Pamela J. VandeVord An automated rat grimace scale for the assessment of pain Scientific Reports |
| title | An automated rat grimace scale for the assessment of pain |
| title_full | An automated rat grimace scale for the assessment of pain |
| title_fullStr | An automated rat grimace scale for the assessment of pain |
| title_full_unstemmed | An automated rat grimace scale for the assessment of pain |
| title_short | An automated rat grimace scale for the assessment of pain |
| title_sort | automated rat grimace scale for the assessment of pain |
| url | https://doi.org/10.1038/s41598-023-46123-x |
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