2025-11-07 東京大学
撮影したマウスの表情から痛みを自動で数値化するAIの構築
<関連情報>
- https://www.a.u-tokyo.ac.jp/topics/topics_20251107-2.html
- https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgaf352/8314858
自由に動くマウスの表情に基づく自動痛み評価 Automated pain assessment based on facial expression of free-moving mice
Koji Kobayashi, Naoaki Sakamoto, Yusuke Miyazaki, Masahito Yamamoto, Takahisa Murata
PNAS Nexus Published:05 November 2025
DOI:https://doi.org/10.1093/pnasnexus/pgaf352
Abstract
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 non-steroidal 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.
