Kasundra Ankit, Chanchlani Roshan, Lal Babu, Thanveeru Suresh K, Ratre Geetesh, Ahmad Reyaz, Sharma Pramod K, Agrawal Amit
Pediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Trauma and Emergency Medicine, All India Institute of Medical Sciences, Bhopal, IND.
Cureus. 2025 Jan 7;17(1):e77074. doi: 10.7759/cureus.77074. eCollection 2025 Jan.
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.
有效的术后疼痛缓解对儿科患者的康复至关重要。虽然人工智能(AI)越来越多地应用于疼痛评估,但关于其在儿童术后疼痛管理中的作用的数据明显不足。本系统综述旨在通过关注AI在预测和评估儿科术后疼痛中的应用来填补这一空白。我们对2000年1月至2023年11月的相关研究进行了全面检索,共识别出4491项研究,根据系统评价和Meta分析的首选报告项目指南将其缩小至8项。这些选定的研究包括使用各种疼痛测量工具评估的4470名儿科患者。所使用的AI模型主要是深度学习和机器学习,准确率在79%至85.62%之间,受试者工作特征曲线下面积值在84.00%至94.00%之间。尽管这些基于AI的疼痛评估工具仍处于早期阶段,但它们通常侧重于单一参数。现有出版物的异质性妨碍了进行Meta分析。我们的研究结果强调了开展多模式、多中心研究以提高基于AI的工具评估儿科人群术后疼痛性能的必要性。此类进展可能会显著提升儿科疼痛管理的未来水平。