An automated rat grimace scale for the assessment of pain

Abstract

Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81–93%. The system’s performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS.

The grimace scale: a useful tool for assessing pain in laboratory animals

“…In addition, attempting to extrapolate certain types of automated analytical models under different conditions, such as variations in administration methods or different drugs, has resulted in low accuracy when tested with actual test data [ 82 ]. Other CNNs in use include You Only Look Once (YOLOv3, YOLOv5), Contrastive Language-Image Pretraining (CLIP), Matlab 2016b, ShuffleNetV2, EfficentNetB0, and MobileNetV3, among others [ 85 , 86 , 87 , 88 , 89 ]. With these limitations in mind, it is currently challenging to achieve a perfect assessment of animal pain using automated technology.…”