Chiang Chih-Yi, Chen Yueh-Peng, Tzeng Hung-Ruei, Chang Man-Hsin, Chiou Lih-Chu, Pei Yu-Cheng
Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan.
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan.
J Pers Med. 2022 May 24;12(6):851. doi: 10.3390/jpm12060851.
Pain assessment is essential for preclinical and clinical studies on pain. The mouse grimace scale (MGS), consisting of five grimace action units, is a reliable measurement of spontaneous pain in mice. However, MGS scoring is labor-intensive and time-consuming. Deep learning can be applied for the automatic assessment of spontaneous pain. We developed a deep learning model, the DeepMGS, that automatically crops mouse face images, predicts action unit scores and total scores on the MGS, and finally infers whether pain exists. We then compared the performance of DeepMGS with that of experienced and apprentice human scorers. The DeepMGS achieved an accuracy of 70-90% in identifying the five action units of the MGS, and its performance (correlation coefficient = 0.83) highly correlated with that of an experienced human scorer in total MGS scores. In classifying pain and no pain conditions, the DeepMGS is comparable to the experienced human scorer and superior to the apprentice human scorers. Heatmaps generated by gradient-weighted class activation mapping indicate that the DeepMGS accurately focuses on MGS-relevant areas in mouse face images. These findings support that the DeepMGS can be applied for quantifying spontaneous pain in mice, implying its potential application for predicting other painful conditions from facial images.
疼痛评估对于疼痛的临床前和临床研究至关重要。小鼠 grimace 量表（MGS）由五个 grimace 动作单元组成，是一种可靠的小鼠自发疼痛测量方法。然而，MGS 评分劳动强度大且耗时。深度学习可用于自发疼痛的自动评估。我们开发了一种深度学习模型 DeepMGS，它能自动裁剪小鼠面部图像，预测 MGS 上的动作单元分数和总分，最终推断是否存在疼痛。然后，我们将 DeepMGS 的性能与经验丰富的人类评分者和新手人类评分者的性能进行了比较。DeepMGS 在识别 MGS 的五个动作单元方面达到了 70 - 90%的准确率，其性能（相关系数 = 0.83）与经验丰富的人类评分者在 MGS 总分方面的性能高度相关。在对疼痛和无疼痛情况进行分类时，DeepMGS 与经验丰富的人类评分者相当，且优于新手人类评分者。梯度加权类激活映射生成的热图表明，DeepMGS 准确地聚焦于小鼠面部图像中与 MGS 相关的区域。这些发现支持 DeepMGS 可用于量化小鼠的自发疼痛，这意味着它在从面部图像预测其他疼痛状况方面具有潜在应用价值。
