In eLife
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.
Yu Huasheng, Xiong Jingwei, Ye Adam Yongxin, Cranfill Suna Li, Cannonier Tariq, Gautam Mayank, Zhang Marina, Bilal Rayan, Park Jong-Eun, Xue Yuji, Polam Vidhur, Vujovic Zora, Dai Daniel, Ong William, Ip Jasper, Hsieh Amanda, Mimouni Nour, Lozada Alejandra, Sosale Medhini, Ahn Alex, Ma Minghong, Ding Long, Arsuaga Javier, Luo Wenqin
2022-Dec-08
mouse, neuroscience
