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

Mice are the most commonly used model animals for itch research and for
the development of anti-itch drugs. Most laboratories 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 trained with frame-labeled mouse
scratching behavior videos, induced by nape injection of chloroquine. 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 the 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
could replace manual quantification for mouse scratching behavior in
different itch models and for drug screening. This dataset includes all
videos for the study to establish a novel deep learning-based system for
automatic mouse scratching behavior quantification.