Automated detection of mouse scratching behaviour using convolutional recurrent neural network.

ABSTRACT: Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.

SUBMITTER: Kobayashi K 

PROVIDER: S-EPMC7803777 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature