Primate Research Institute.
Department of Advanced Mathematical Sciences, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
J Am Assoc Lab Anim Sci. 2024 Jul 1;63(4):403-411. doi: 10.30802/AALAS-JAALAS-23-000056. Epub 2024 Mar 1.
Facial expressions have increasingly been used to assess emotional states in mammals. The recognition of pain in research animals is essential for their well-being and leads to more reliable research outcomes. Automating this process could contribute to early pain diagnosis and treatment. Artificial neural networks have become a popular option for image classification tasks in recent years due to the development of deep learning. In this study, we investigated the ability of a deep learning model to detect pain in Japanese macaques based on their facial expression. Thirty to 60 min of video footage from Japanese macaques undergoing laparotomy was used in the study. Macaques were recorded undisturbed in their cages before surgery (No Pain) and one day after the surgery before scheduled analgesia (Pain). Videos were processed for facial detection and image extraction with the algorithms RetinaFace (adding a bounding box around the face for image extraction) or Mask R-CNN (contouring the face for extraction). ResNet50 used 75% of the images to train systems; the other 25% were used for testing. Test accuracy varied from 48 to 54% after box extraction. The low accuracy of classification after box extraction was likely due to the incorporation of features that were not relevant for pain (for example, background, illumination, skin color, or objects in the enclosure). However, using contour extraction, preprocessing the images, and fine-tuning, the network resulted in 64% appropriate generalization. These results suggest that Mask R-CNN can be used for facial feature extractions and that the performance of the classifying model is relatively accurate for nonannotated single-frame images.
面部表情在评估哺乳动物的情绪状态方面得到了越来越多的应用。在研究动物中识别疼痛对于它们的福祉至关重要，并能得出更可靠的研究结果。实现这一过程自动化有助于早期诊断和治疗疼痛。由于深度学习的发展，人工神经网络在近年来成为图像分类任务的热门选择。在这项研究中，我们调查了深度学习模型根据日本猕猴的面部表情来检测疼痛的能力。研究使用了 30 到 60 分钟的日本猕猴接受剖腹手术的视频片段。猕猴在手术前（无疼痛）和手术一天后在预定的镇痛之前（疼痛）在笼子里不受干扰地被记录。使用算法 RetinaFace（为图像提取添加一个包围面部的边界框）或 Mask R-CNN（为提取勾勒面部轮廓）对视频进行面部检测和图像提取处理。ResNet50 使用 75%的图像来训练系统；其余的 25%用于测试。使用边界框提取后的测试准确率从 48%到 54%不等。边界框提取后的分类准确率低可能是因为纳入了与疼痛无关的特征（例如背景、照明、肤色或围栏内的物体）。然而，通过使用轮廓提取、预处理图像和微调，网络的泛化准确率达到了 64%。这些结果表明，Mask R-CNN 可用于面部特征提取，并且分类模型对于未注释的单帧图像的性能相对准确。
