A Deep Learning Approach for Infant Pain Assessment Using Facial Expressions Through Convolutional Neural Network

• Published in: Computers, informatics, nursing Vol. 43; no. 7
• Main Authors: Zhang, Long, Zhu, Ting Yan, Zhang, Ying
• Format: Journal Article
• Language: English
• Published: United States 27.03.2025
• Subjects: Convolutional Neural Networks; Deep Learning; Facial Expression; Humans; Infant; Infant, Newborn; Neural Networks, Computer; Pain Measurement - methods; Deep learning; Computer; Infant; Nursing assessment; Neural networks; Pain measurement
• DOI: 10.1097/CIN.0000000000001302

This study presents a deep learning-based approach for assessing infant pain through facial expression analysis using Convolutional Neural Networks (CNNs). Given infants' inability to verbally articulate pain, reliable assessment methods are crucial in clinical nursing. To address this need, we developed a CNN model utilizing the COPE (Classification of Pain Expression) database. Our model achieved a test accuracy of 90.24%, with an average precision and recall of 87.58%, and an F1 score of 0.8758. Additionally, the model demonstrated high performance with an area under the curve of 0.9818 on the receiver operating characteristic curve. These results underscore the potential utility of CNNs for providing an objective pain assessment in clinical settings. However, the study acknowledges limitations, including a small sample size, the need for external validation, and ethical considerations. Future research should focus on expanding the dataset, conducting external validation, refining model architectures, and addressing ethical considerations to enhance performance and applicability. These efforts will advance infant pain management, ensure ethical integrity, and improve the overall quality of care.