Automated scratching detection system for black mouse using deep learning

Abstract

The evaluation of scratching behavior is important in experimental animals because there is significant interest in elucidating mechanisms and developing medications for itching. The scratching behavior is classically quantified by human observation, but it is labor-intensive and has low throughput. We previously established an automated scratching detection method using a convolutional recurrent neural network (CRNN). The established CRNN model was trained by white mice (BALB/c), and it could predict their scratching bouts and duration. However, its performance in black mice (C57BL/6) is insufficient. Here, we established a model for black mice to increase prediction accuracy. Scratching behavior in black mice was elicited by serotonin administration, and their behavior was recorded using a video camera. The videos were carefully observed, and each frame was manually labeled as scratching or other behavior. The CRNN model was trained using the labels and predicted the first-look videos. In addition, posterior filters were set to remove unlikely short predictions. The newly trained CRNN could sufficiently detect scratching behavior in black mice (sensitivity, 98.1%; positive predictive rate, 94.0%). Thus, our established CRNN and posterior filter successfully predicted the scratching behavior in black mice, highlighting that our workflow can be useful, regardless of the mouse strain.

Keywords: convolutional neural network; itching; neural network; pruritus; scratching behavior.

FIGURE 1

Performance validation and filter application. (A,B) The comparison of the number of scratching bouts (A) and duration (B) between human observation and pre-filtered predictions. (C) Details of mis-predicted frames in pre-filtered predictions. (D) Schematic figure of the posterior filter. (E,F) The comparison of the number of scratching bouts (E) and duration (F) between human observation and post-filtered predictions. (G) Details of mis-predicted frames in post-filtered predictions.

FIGURE 2

Performance evaluation using the test dataset (A,B) The comparison of the number of scratching bouts (A) and duration (B) between human observation and predictions. (C) Details of mis-predicted frames.