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Automated scratching detection system for black mouse using deep learning
The evaluation of scratching behavior is important in experimental animals because there is significant interest in elucidating mechanisms and developing medications for itching. The scratching behavior is classically quantified by human observation, but it is labor-intensive and has low throughput....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352956/ https://www.ncbi.nlm.nih.gov/pubmed/35936901 http://dx.doi.org/10.3389/fphys.2022.939281 |
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author | Sakamoto, Naoaki Haraguchi, Taiga Kobayashi, Koji Miyazaki, Yusuke Murata, Takahisa |
author_facet | Sakamoto, Naoaki Haraguchi, Taiga Kobayashi, Koji Miyazaki, Yusuke Murata, Takahisa |
author_sort | Sakamoto, Naoaki |
collection | PubMed |
description | The evaluation of scratching behavior is important in experimental animals because there is significant interest in elucidating mechanisms and developing medications for itching. The scratching behavior is classically quantified by human observation, but it is labor-intensive and has low throughput. We previously established an automated scratching detection method using a convolutional recurrent neural network (CRNN). The established CRNN model was trained by white mice (BALB/c), and it could predict their scratching bouts and duration. However, its performance in black mice (C57BL/6) is insufficient. Here, we established a model for black mice to increase prediction accuracy. Scratching behavior in black mice was elicited by serotonin administration, and their behavior was recorded using a video camera. The videos were carefully observed, and each frame was manually labeled as scratching or other behavior. The CRNN model was trained using the labels and predicted the first-look videos. In addition, posterior filters were set to remove unlikely short predictions. The newly trained CRNN could sufficiently detect scratching behavior in black mice (sensitivity, 98.1%; positive predictive rate, 94.0%). Thus, our established CRNN and posterior filter successfully predicted the scratching behavior in black mice, highlighting that our workflow can be useful, regardless of the mouse strain. |
format | Online Article Text |
id | pubmed-9352956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93529562022-08-06 Automated scratching detection system for black mouse using deep learning Sakamoto, Naoaki Haraguchi, Taiga Kobayashi, Koji Miyazaki, Yusuke Murata, Takahisa Front Physiol Physiology The evaluation of scratching behavior is important in experimental animals because there is significant interest in elucidating mechanisms and developing medications for itching. The scratching behavior is classically quantified by human observation, but it is labor-intensive and has low throughput. We previously established an automated scratching detection method using a convolutional recurrent neural network (CRNN). The established CRNN model was trained by white mice (BALB/c), and it could predict their scratching bouts and duration. However, its performance in black mice (C57BL/6) is insufficient. Here, we established a model for black mice to increase prediction accuracy. Scratching behavior in black mice was elicited by serotonin administration, and their behavior was recorded using a video camera. The videos were carefully observed, and each frame was manually labeled as scratching or other behavior. The CRNN model was trained using the labels and predicted the first-look videos. In addition, posterior filters were set to remove unlikely short predictions. The newly trained CRNN could sufficiently detect scratching behavior in black mice (sensitivity, 98.1%; positive predictive rate, 94.0%). Thus, our established CRNN and posterior filter successfully predicted the scratching behavior in black mice, highlighting that our workflow can be useful, regardless of the mouse strain. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9352956/ /pubmed/35936901 http://dx.doi.org/10.3389/fphys.2022.939281 Text en Copyright © 2022 Sakamoto, Haraguchi, Kobayashi, Miyazaki and Murata. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Sakamoto, Naoaki Haraguchi, Taiga Kobayashi, Koji Miyazaki, Yusuke Murata, Takahisa Automated scratching detection system for black mouse using deep learning |
title | Automated scratching detection system for black mouse using deep learning |
title_full | Automated scratching detection system for black mouse using deep learning |
title_fullStr | Automated scratching detection system for black mouse using deep learning |
title_full_unstemmed | Automated scratching detection system for black mouse using deep learning |
title_short | Automated scratching detection system for black mouse using deep learning |
title_sort | automated scratching detection system for black mouse using deep learning |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352956/ https://www.ncbi.nlm.nih.gov/pubmed/35936901 http://dx.doi.org/10.3389/fphys.2022.939281 |
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