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A Deep Learning Approach for Infant Pain Assessment Using Facial Expressions Through Convolutional Neural Network
Long Zhang1, Ting Yan Zhu, Ying Zhang
1Author Affiliations: Nursing School of Kunming Medical University, Kunming (Zhang); Kunming Children's Hospital (Dr Zhu); and Chuxiong Medical College, Chuxiong; and Department of Physiology, School of Basic Medicine, Kunming Medical University (Dr Zhang), Kunming, Yunnan, China.
This study uses deep learning and Convolutional Neural Networks (CNNs) to objectively assess infant pain via facial expressions. The CNN model achieved 90.24% accuracy, showing promise for clinical pain management.
Area of Science:
- Medical technology
- Artificial intelligence in healthcare
- Pediatric nursing
Background:
- Infants cannot verbally express pain, necessitating objective pain assessment tools in clinical nursing.
- Current pain assessment methods for infants may lack objectivity and consistency.
- Developing reliable methods is crucial for effective infant pain management and care.
Purpose of the Study:
- To develop and evaluate a deep learning model for objective infant pain assessment using facial expressions.
- To leverage Convolutional Neural Networks (CNNs) for analyzing infant facial cues indicative of pain.
- To determine the efficacy of the proposed CNN model in a clinical nursing context.
Main Methods:
- A Convolutional Neural Network (CNN) model was developed and trained using the COPE (Classification of Pain Expression) database.
- Facial expression data from infants was analyzed to detect and classify pain indicators.
- The model's performance was evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC curve analysis.
Main Results:
- The CNN model achieved a high test accuracy of 90.24%.
- The model demonstrated strong performance with an average precision and recall of 87.58% and an F1 score of 0.8758.
- The receiver operating characteristic (ROC) curve analysis yielded an area under the curve (AUC) of 0.9818, indicating excellent discriminative ability.
Conclusions:
- Deep learning-based facial expression analysis using CNNs shows significant potential for objective infant pain assessment in clinical settings.
- The developed CNN model offers a promising tool to aid healthcare professionals in accurately identifying and managing infant pain.
- Further research involving larger datasets, external validation, and ethical considerations is recommended to enhance the model's clinical applicability and reliability.
