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

## Description of the Data and file structure

In the videos.zip folder, there are five subfolders:

1. Training_test_videos subfolder: This folder includes 40 videos for training the model, V1-V32 are videos for training, and V33-V40 are videos for validation and testing.
2. test_videos_SADBE_chronic_itch subfolder: This folder includes 9 videos V1-V9 for testing model performance in chronic itch.
3. test_videos_histamine_nape subfolder: This folder includes 4 videos V1-V4 for testing model performance in histamine-induced acute itch in the nape.
4. test_videos_CQ_cheek subfolder: This folder includes 7 videos V1-V7 for testing model performance in CQ-induced acute itch in the cheek.
5. test_videos_anti-itch subfolder: This folder includes two subfolders: In the anti-itch subfolder, there are 6 videos V1-V6; and in the control subfolder, there are 7 video...