Automated pain assessment based on facial expression of free-moving mice

Pain is a basic sensation associated with tissue injury. Although facial expression is a useful indicator of pain in mammals, its assessment in rodents requires expertise and experience. Here, we aimed to establish an automated pain assessment method using the facial images of free-moving mice. A convolutional neural network (CNN) was trained with the facial images of untreated mice and those subjected to acetic acid (AC)-induced pain. The trained CNN successfully predicted the faces of AC-, capsaicin-, and calcitonin gene-related peptide-induced pain that had not been used for CNN training. It also detected the analgesic effect of diclofenac, a nonsteroidal anti-inflammatory drug, against AC-induced pain. We used dimensionality reduction algorithms to select images with similar compositions and visualized the regions focused on by the CNN during predictions. The CNN focused on the head, forehead, ear, eye, cheek, and nose to predict pain or no pain. In conclusion, we established a method for automated pain assessment using the facial images of free-moving mice.