Ramdhanie Gusgus Ghraha, Wanda Dessie, Agustini Nur, Abuzairi Tomy
Department of Pediatric and Fundamental Nursing, Faculty of Nursing, Universitas Padjadjaran, Bandung, West Java, Indonesia.
Doctoral Nursing Program, Faculty of Nursing, Universitas Indonesia, Depok, West Java, Indonesia.
BMC Nurs. 2025 Jul 11;24(1):905. doi: 10.1186/s12912-025-03451-9.
Pain management in children remains a significant challenge due to the lack of appropriate assessment methods. Facial expression-based instruments are widely used as facial expressions serve as a key nonverbal indicator of pain. However, conventional paper-based tools have limitations, including subjective interpretation, observer bias, and low accuracy. To address these challenges, digital technology-based facial recognition systems have emerged as a more objective and reliable alternative. This study aims to identify technology-based models and evaluate the efficacy of digital pain facial expression assessment tools for children. These technology-driven approaches aim to provide more objective and consistent solutions than conventional methods.
This study aims to identify the technology-based models and efficacy of digital-based pain facial expression assessment instruments in children with a systematic review approach.
This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The article search used five databases: PubMed, EBSCOhost, ScienceDirect, Scopus, and Google Scholar. The study questions used the PCC (Population, Concept, and Context) research framework guidelines. Children with pain as a population, assessment of pain facial expressions as a concept, and technological efficacy as a context. The inclusion criteria for this study were articles published from 2015 to 2024, full-text and free-text articles, and studies that focused on assessing facial expressions of pain in children. Studies were excluded if the article was not in English, and the research design was a literature review type. Study quality was assessed using the Critical Appraisal Checklist Tools from the Joanna Briggs Institute (JBI).
We found 18 studies that described the technology model for assessing facial expressions of pain using computers and mobile applications through video and image recordings. Overall, this suggests that the model used to assess facial expressions of pain is more effective than conventional or paper-based pain assessments. The developed technology model has many advantages, including good performance, high accuracy, an excellent program, validity, reliability, high sensitivity, specificity, and more sensitive.
The findings of this study demonstrate that technology-based models for facial expression pain assessment provide a more objective, accurate, and efficient alternative to conventional methods. These digital tools, including computer and mobile applications, offer real-time analysis, reduce observer bias, and enhance consistency in pain evaluation. Their accessibility, convenience, and automation further strengthen their potential to revolutionize pediatric pain assessment, addressing the limitations of traditional paper-based approaches. Future research should focus on refining these models to improve accuracy across diverse pediatric populations.
由于缺乏合适的评估方法，儿童疼痛管理仍然是一项重大挑战。基于面部表情的工具被广泛使用，因为面部表情是疼痛的关键非语言指标。然而，传统的纸质工具存在局限性，包括主观解读、观察者偏差和低准确性。为应对这些挑战，基于数字技术的面部识别系统已成为一种更客观、可靠的替代方法。本研究旨在识别基于技术的模型，并评估数字疼痛面部表情评估工具对儿童的有效性。这些技术驱动的方法旨在提供比传统方法更客观、一致的解决方案。
本研究旨在通过系统评价方法识别基于技术的模型以及数字疼痛面部表情评估工具在儿童中的有效性。
本系统评价遵循系统评价和Meta分析的首选报告项目（PRISMA）。文章检索使用了五个数据库：PubMed、EBSCOhost、ScienceDirect、Scopus和谷歌学术。研究问题采用PCC（人群、概念和背景）研究框架指南。以疼痛儿童为人群，疼痛面部表情评估为概念，技术有效性为背景。本研究的纳入标准为2015年至2024年发表的文章、全文和自由文本文章，以及专注于评估儿童疼痛面部表情的研究。如果文章不是英文的，且研究设计为文献综述类型，则排除该研究。使用乔安娜·布里格斯研究所（JBI）的批判性评估清单工具评估研究质量。
我们发现18项研究描述了通过视频和图像记录使用计算机和移动应用程序评估疼痛面部表情的技术模型。总体而言，这表明用于评估疼痛面部表情的模型比传统或纸质疼痛评估更有效。所开发的技术模型具有许多优点，包括性能良好、准确性高、程序优秀、有效性、可靠性、高敏感性、特异性以及更灵敏。
本研究结果表明，基于技术的面部表情疼痛评估模型为传统方法提供了一种更客观、准确和高效的替代方法。这些数字工具，包括计算机和移动应用程序，提供实时分析，减少观察者偏差，并提高疼痛评估的一致性。它们的可及性、便利性和自动化进一步增强了其变革儿科疼痛评估的潜力，克服了传统纸质方法的局限性。未来的研究应专注于完善这些模型，以提高在不同儿科人群中的准确性。
