Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracy
- University of Pennsylvania, United States
- University of California, Davis, United States
- Howard Hughes Medical Institute, Harvard Medical School, United States
- Massachusetts Institute of Technology, United States
Abstract
Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most labs manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network (CRNN) trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine (CQ). The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that is ready to replace manual quantification for mouse scratching behavior in different itch models and for drug screening.
Data availability
The training and test videos generated during the current study can be downloaded from DRYAD (https://doi.org/10.5061/dryad.mw6m9060s). The codes for model training and test can be downloaded from GitHub (https://github.com/taimeimiaole/Scratch-AID)
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Data From: Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracyDryad Digital Repository, doi:10.5061/dryad.mw6m9060s.
Article and author information
Author details
Funding
National Science Foundation (DMS-1854770)
- Javier Arsuaga
National Institutes of Health (R01 NS083702)
- Wenqin Luo
National Institutes of Health (R34 NS118411)
- Long Ding
- Wenqin Luo
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Brian S Kim MD, Icahn School of Medicine at Mount Sinai, United States
Ethics
Animal experimentation: Mice were housed in the John Morgan animal facility at the University of Pennsylvania. All animal treatments were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee and the guidelines of the National Institutes of Health (Protocol No. 804886).
Version history
- Preprint posted: October 7, 2022 (view preprint)
- Received: October 9, 2022
- Accepted: November 29, 2022
- Accepted Manuscript published: December 8, 2022 (version 1)
- Version of Record published: December 19, 2022 (version 2)
Copyright
© 2022, Yu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracy
eLife 11:e84042.
https://doi.org/10.7554/eLife.84042
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