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

  • Zhiheng Feng1
  • Mingcai Chen2
  • Jue Zhang3
  • Xin Peng4,*,

1Academy for Advanced Interdisciplinary Studies, Peking University, 100871 Beijing, China

2State Key Laboratory for Novel Software Technology, Nanjing University, 210023 Nanjing, Jiangsu, China

3College of Engineering, Peking University, 100871 Beijing, China

4Department of Oral and Maxillofacial Surgery, Peking University School of Stomatology, 100081 Beijing, China

DOI: 10.22514/jofph.2024.008 Vol.38,Issue 1,March 2024 pp.77-92

Submitted: 13 April 2023 Accepted: 10 November 2023

Published: 12 March 2024

*Corresponding Author(s): Xin Peng E-mail:


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.


Oral diseases; Trigeminal neuralgia; Pain assessment; Convolutional neural networks

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Zhiheng Feng,Mingcai Chen,Jue Zhang,Xin Peng. An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model. Journal of Oral & Facial Pain and Headache. 2024. 38(1);77-92.


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