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Papers of the Week

Papers: 3 Dec 2022 - 9 Dec 2022

2022 Dec 08



Editor's Pick

Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracy.


Yu H, Xiong J, Ye A Y, Cranfill S L, Cannonier T, Gautam M, Zhang M, Bilal R, Park J-E, Xue Y, Polam V, Vujovic Z, Dai D, Ong W, Ip J, Hsieh A, Mimouni N, Lozada A, Sosale M, Ahn A, et al.
Elife. 2022 Dec 08; 11.
PMID: 36476338.


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.