Related Experiment Video
Updated: Jul 4, 2025
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned
Jian-Ming Yue1, Qi Wang1, Bin Liu1
1Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
Insights
Accurate pain assessment in children is challenging. This study proposes using artificial intelligence (AI) to analyze facial expressions for improved pediatric perioperative pain management and patient outcomes.
Area of Science:
- Pediatric Anesthesiology
- Medical Imaging
- Artificial Intelligence
Background:
- Pediatric perioperative pain assessment tools are lacking.
- Children's inability to articulate pain leads to management challenges.
- Early pain exposure has detrimental short- and long-term health consequences.
Purpose of the Study:
- To develop an AI-powered software for automatic facial pain expression recognition in children.
- To enhance the accuracy and reliability of pediatric perioperative pain assessment.
- To improve the quality of life for pediatric patients undergoing surgery.
Main Methods:
- Utilizing artificial intelligence (AI) and machine learning, specifically deep convolutional neural networks.
- Developing a large database of children's facial expressions during perioperative pain.
- Analyzing subtle facial features for systematic pain expression recognition.
Main Results:
- AI technology demonstrates high capability in processing deep facial models for image analysis.
- The proposed software can effectively identify and analyze subtle facial pain indicators.
- Potential for systematic and objective pain assessment in pediatric patients.
Conclusions:
- AI-driven facial expression analysis offers a promising solution for pediatric pain assessment.
- This technology can significantly improve perioperative pain management in children.
- Enhanced pain management is expected to positively impact pediatric patient outcomes and long-term health.
Abstract:
Although the pediatric perioperative pain management has been improved in recent years, the valid and reliable pain assessment tool in perioperative period of children remains a challenging task. Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults, but also because there is a lack of effective and specific assessment tool for children. In addition, exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae. The short-term sequelae can induce a series of neurological, endocrine, cardiovascular system stress related to psychological trauma, while long-term sequelae may alter brain maturation process, which can lead to impair neurodevelopmental, behavioral, and cognitive function. Children's facial expressions largely reflect the degree of pain, which has led to the developing of a number of pain scoring tools that will help improve the quality of pain management in children if they are continually studied in depth. The artificial intelligence (AI) technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks, which can effectively identify and systematically analyze various subtle features of children's facial expressions. Based on the construction of a large database of images of facial expressions in children with perioperative pain, this study proposes to develop and apply automatic facial pain expression recognition software using AI technology. The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.
