Background: Pain assessment in individuals with neurological conditions, especially those with limited self-report ability and altered facial expressions, presents challenges. Existing measures, relying on direct observation by caregivers, lack sensitivity and specificity. In cerebral palsy, pain is a common comorbidity and a reliable evaluation protocol is crucial. Thus, having an automatic system that recognizes facial expressions could be of enormous help when diagnosing pain in this type of patient. Objectives: 1) to build a dataset of facial pain expressions in individuals with cerebral palsy, and 2) to develop an automated facial recognition system based on deep learning for pain assessment addressed to this population. Methods: Ten neural networks were trained on three pain image databases, including the UNBC-McMaster Shoulder Pain Expression Archive Database, the Multimodal Intensity Pain Dataset, and the Delaware Pain Database. Additionally, a curated dataset (CPPAIN) was created, consisting of 109 preprocessed facial pain expression images from individuals with cerebral palsy, categorized by two physiotherapists using the Facial Action Coding System observational scale. Results: InceptionV3 exhibited promising performance on the CP-PAIN dataset, achieving an accuracy of 62.67% and an F1 score of 61.12%. Explainable artificial intelligence techniques revealed consistent essential features for pain identification across models. Conclusion: This study demonstrates the potential of deep learning models for robust pain detection in populations with neurological conditions and communication disabilities. The creation of a larger dataset specific to cerebral palsy would further enhance model accuracy, offering a valuable tool for discerning subtle and idiosyncratic pain expressions. The insights gained could extend to other complex neurological conditions.

翻译：背景：神经系统疾病患者的疼痛评估，尤其是那些自我报告能力受限且面部表情异常的患者，具有挑战性。现有依赖于护理人员直接观察的评估方法缺乏敏感性和特异性。脑瘫患者中疼痛是常见的共病，因此建立可靠的评估方案至关重要。针对该类患者，开发能够识别面部表情的自动化系统将极大有助于疼痛诊断。目标：1）构建脑瘫患者疼痛面部表情数据集，2）开发基于深度学习的面向该人群的自动化面部识别疼痛评估系统。方法：基于三个疼痛图像数据库（包括UNBC-McMaster肩痛表情档案数据库、多模态强度疼痛数据集和特拉华疼痛数据库）训练了十种神经网络。此外，创建了一个精选数据集（CPPAIN），包含109张来自脑瘫患者的预处理后面部疼痛表情图像，由两位物理治疗师采用面部动作编码系统观察量表进行分类。结果：InceptionV3在CP-PAIN数据集上表现优异，准确率达62.67%，F1得分为61.12%。可解释人工智能技术揭示了跨模型疼痛识别的关键一致性特征。结论：本研究表明深度学习模型在神经系统疾病和沟通障碍人群中进行稳健疼痛检测的潜力。创建针对脑瘫的更大规模数据集将进一步提升模型精度，为识别细微且特异的疼痛表情提供有价值的工具。研究获得的见解可推广至其他复杂神经系统疾病。