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Updated: Jul 10, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)
Published on: January 27, 2010
Role of Artificial Intelligence in the Assessment of Postoperative Pain in the Pediatric Population: A Systematic
Ankit Kasundra1, Roshan Chanchlani1, Babu Lal2
1Pediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Insights
Artificial intelligence (AI) shows promise in assessing pediatric postoperative pain, with models achieving 79-85% accuracy. Further research is needed to enhance AI tools for better pain management in children.
Area of Science:
- Medical Informatics
- Pediatric Anesthesiology
- Artificial Intelligence in Healthcare
Background:
- Effective postoperative pain management is vital for pediatric patient recovery.
- Artificial intelligence (AI) is emerging in pain assessment, but its role in pediatric postoperative pain is under-researched.
- A systematic review is needed to consolidate current data on AI for pediatric postoperative pain.
Purpose of the Study:
- To systematically review the current literature on the application of AI in predicting and evaluating pediatric postoperative pain.
- To identify the types of AI models used and their performance metrics in pediatric pain assessment.
- To highlight gaps in research and suggest future directions for AI in pediatric pain management.
Main Methods:
- A comprehensive literature search was conducted from January 2000 to November 2023.
- Studies were selected based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
- Included studies involved pediatric patients and utilized AI for pain assessment, with data analyzed for AI model types and performance.
Main Results:
- Eight studies involving 4,470 pediatric patients met the inclusion criteria.
- AI models, mainly deep learning and machine learning, reported accuracy rates from 79% to 85.62%.
- Area under the receiver operating characteristic curve (AUC) values ranged from 84.00% to 94.00%, indicating good performance.
Conclusions:
- Current AI-based tools for pediatric postoperative pain assessment are in early development and often focus on single parameters.
- The heterogeneity of studies precluded a meta-analysis, indicating a need for standardized research.
- Multimodal, multicentric research is essential to improve AI performance and advance pediatric pain management.
Abstract:
Effective postoperative pain relief is crucial for the recovery of pediatric patients. While artificial intelligence (AI) is increasingly being applied in pain assessment, there is a notable lack of data regarding its role in managing postoperative pain in children. This systematic review aims to address this gap by focusing on AI's use in predicting and evaluating pediatric postoperative pain. We conducted a comprehensive search of relevant studies from January 2000 to November 2023, identifying 4,491 studies, which were narrowed down to eight based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. These selected studies included 4,470 pediatric patients assessed using various pain measurement tools. The AI models used, primarily deep learning and machine learning, demonstrated accuracy rates ranging from 79% to 85.62% and area under the receiver operating characteristic curve values between 84.00% and 94.00%. Although these AI-based pain assessment tools are still in the early stages, they often focus on single parameters. The heterogeneity of the available publications prevented the conduct of a meta-analysis. Our findings underscore the need for multimodal, multicentric research to improve the performance of AI-based tools for assessing postoperative pain in the pediatric population. Such advancements could significantly enhance the future of pediatric pain management.
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