2024/01/22 by Álvaro Sabater-Gárriz, F. Xavier Gaya-Morey, Sabater-Gárriz, Álvaro +9 · 3 citations
Medicine · #Artificial intelligence #Botulinum Toxin and Related Neurological Disorders #Cerebral Palsy and Movement Disorders #Cerebral palsy #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #FOS: Computer and information sciences #Facial Action Coding System #Facial expression #Machine learning #Medicine #Nerve Injury and Rehabilitation #Palsy #Pathology #Physical medicine and rehabilitation #Physical therapy
paper · pdf · doi:10.48550/arxiv.2401.12161
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/01/22 · openalex created_date 2024/01/24 · openalex updated_date 2026/08/01
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.