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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 (\n                    <jats:bold>\n                      <jats:underline>A<\/jats:underline>\n                    <\/jats:bold>\n                    utomatic\n                    <jats:bold>\n                      <jats:underline>I<\/jats:underline>\n                    <\/jats:bold>\n                    tch\n                    <jats:bold>\n                      <jats:underline>D<\/jats:underline>\n                    <\/jats:bold>\n                    etection), 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 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 could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.\n                  <\/jats:p>","DOI":"10.7554\/elife.84042","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T06:00:20Z","timestamp":1670479220000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.84042","source":"Crossref","is-referenced-by-count":14,"title":["Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy"],"prefix":"10.7554","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-2512","authenticated-orcid":true,"given":"Huasheng","family":"Yu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00b30xv10","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Neuroscience, Perelman School of Medicine, University of 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