Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracy.
Huasheng YuJingwei XiongAdam Yongxin YeSuna Li CranfillTariq CannonierMayank GautamMarina ZhangRayan BilalJong-Eun ParkYuji XueVidhur PolamZora VujovicDaniel DaiWilliam OngJasper IpAmanda HsiehNour MimouniAlejandra LozadaMedhini SosaleAlex AhnMinghong MaLong DingJavier ArsuagaWenqin LuoPublished in: eLife (2022)
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.
Keyphrases
- deep learning
- neural network
- atopic dermatitis
- machine learning
- emergency department
- drug induced
- type diabetes
- computed tomography
- artificial intelligence
- gene expression
- dna methylation
- loop mediated isothermal amplification
- high intensity
- genome wide
- skeletal muscle
- hepatitis b virus
- ultrasound guided
- sensitive detection
- positron emission tomography
- network analysis
- wild type