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Assessment of postoperative pain in children with computer assisted facial expression analysis
Ayla İrem Aydın1, Nurcan Özyazıcıoğlu1
1Department of Nursing, Faculty of Health Science, Bursa Uludag University, 16000 Bursa, Turkey.
Insights
Computer-aided facial expression analysis accurately estimates children's postoperative pain. This machine learning approach offers a reliable tool for assessing pain severity in pediatric patients.
Area of Science:
- Pediatric Surgery
- Pain Management
- Computer Vision
Background:
- Assessing postoperative pain in children is challenging.
- Objective pain measurement tools are needed for accurate pain management.
- Facial expressions are key indicators of pain in children.
Purpose of the Study:
- To evaluate computer-aided facial expression analysis for assessing postoperative pain in children.
- To determine the reliability of machine learning algorithms in pain severity estimation.
- To explore the potential of this technology in clinical practice.
Main Methods:
- Methodological observational study involving 83 children (7-18 years).
- Data collected using Wong Baker Faces pain rating scale and Visual Analog Scale.
- Facial action units analyzed using OpenFace and machine learning algorithms in Python.
Main Results:
- Machine learning pain prediction closely aligned with child's self-reported pain.
- Order of accuracy for pain assessment was machine prediction, mother, then nurse.
- The system reliably coded facial expressions and measured pain-related facial action units.
Conclusions:
- Machine learning-based facial expression analysis is effective for estimating pediatric pain severity.
- This technology can serve as a scalable, standard, and valid pain assessment method for nurses.
- Facial expression analysis holds significant potential for improving pain management in children.
Purpose:
The present study was conducted to evaluate the use of computer-aided facial expression analysis to assess postoperative pain in children.
Design And Methods:
This was a methodological observational study. The study population consisted of patients in the age group of 7-18 years who underwent surgery in the pediatric surgery clinic of a university hospital. The study sample consisted of 83 children who agreed to participate and met the sample selection criteria. Data were collected by the researcher using the Wong Baker Faces pain rating scale and Visual Analog Scale. Data were collected from the child, mother, nurse, and one external observer. Facial action units associated with pain were used for machine estimation. OpenFace was used to analyze the child's facial action units and Python was used for machine learning algorithms. The intraclass correlation coefficient was used for statistical analysis of the data.
Results:
The pain score predicted by the machine and the pain score assessments of the child, mother, nurse, and observer were compared. The pain assessment closest to the self-reported pain score by the child was in the order of machine prediction, mother, and nurse.
Conclusions:
The machine learning method used in pain assessment in children performed well in estimating pain severity.It can code facial expressions of children's pain and reliably measure pain-related facial action units from video recordings.
Application To Practice:
The machine learning method for facial expression analysis assessed in this study can potentially be used as a scalable, standard, and valid pain assessment method for nurses in clinical practice.
