An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model

Abstract

Pain assessment in trigeminal neuralgia (TN) mouse models is essential for exploring its pathophysiology and developing effective analgesics. However, pain assessment methods for TN mouse models have not been widely studied, resulting in a critical gap in our understanding of TN. With the rapid advancement of deep learning, numerous pain assessment methods based on deep learning have emerged. Nonetheless, these methods have some limitations: (1) insufficiently objective supervision signals for training, (2) failure to account for the dynamic behavioral characteristics of mouse models in the constructed models and (3) inadequate generalization ability of the models. In this study, we initially constructed an objective pain grading dataset as the ground truth for model training, which remedy the limitations of prior studies that relied on subjective evaluation as supervisory signals. Then we proposed a novel deep neural network, named trigeminal neuralgia pain assessment network (TNPAN), which fuses the static texture characteristics and dynamic behavioral characteristics of mouse facial expressions. The promising experimental results demonstrate that TNPAN exhibits exceptional accuracy and generalization capability in pain assessment.