Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
BMJ Open. 2019 Dec 11;9(12):e030482. doi: 10.1136/bmjopen-2019-030482.
Infants can experience pain similar to adults, and improperly controlled pain stimuli could have a long-term adverse impact on their cognitive and neurological function development. The biggest challenge of achieving good infant pain control is obtaining objective pain assessment when direct communication is lacking. For years, computer scientists have developed many different facial expression-centred machine learning (ML) methods for automatic infant pain assessment. Many of these ML algorithms showed rather satisfactory performance and have demonstrated good potential to be further enhanced for implementation in real-world clinical settings. To date, there is no prior research that has systematically summarised and compared the performance of these ML algorithms. Our proposed meta-analysis will provide the first comprehensive evidence on this topic to guide further ML algorithm development and clinical implementation.
We will search four major public electronic medical and computer science databases including Web of Science, PubMed, Embase and IEEE Xplore Digital Library from January 2008 to present. All the articles will be imported into the Covidence platform for study eligibility screening and inclusion. Study-level extracted data will be stored in the Systematic Review Data Repository online platform. The primary outcome will be the prediction accuracy of the ML model. The secondary outcomes will be model utility measures including generalisability, interpretability and computational efficiency. All extracted outcome data will be imported into RevMan V.5.2.1 software and R V3.3.2 for analysis. Risk of bias will be summarised using the latest Prediction Model Study Risk of Bias Assessment Tool.
This systematic review and meta-analysis will only use study-level data from public databases, thus formal ethical approval is not required. The results will be disseminated in the form of an official publication in a peer-reviewed journal and/or presentation at relevant conferences.
CRD42019118784.
婴儿能够感受到与成人相似的疼痛，而不当控制的疼痛刺激可能对其认知和神经功能发育产生长期的不良影响。实现良好婴儿疼痛控制的最大挑战是在缺乏直接沟通的情况下获得客观的疼痛评估。多年来，计算机科学家们开发了许多不同的基于面部表情的机器学习（ML）方法，用于自动评估婴儿的疼痛。这些 ML 算法中的许多都表现出相当令人满意的性能，并已证明具有很好的潜力，可以进一步增强，以便在实际临床环境中实施。迄今为止，尚无研究系统地总结和比较这些 ML 算法的性能。我们提出的荟萃分析将为该主题提供首个全面的证据，以指导进一步的 ML 算法开发和临床实施。
我们将从 2008 年 1 月至今，在四个主要的公共电子医学和计算机科学数据库（包括 Web of Science、PubMed、Embase 和 IEEE Xplore Digital Library）中搜索文献。所有文章都将被导入 Covidence 平台进行研究资格筛选和纳入。研究水平提取的数据将被存储在系统性综述数据存储库在线平台中。主要结果将是 ML 模型的预测准确性。次要结果将是模型效用指标，包括通用性、可解释性和计算效率。所有提取的结果数据都将被导入 RevMan V.5.2.1 软件和 R V3.3.2 进行分析。风险偏倚将使用最新的预测模型研究风险偏倚评估工具进行总结。
本系统评价和荟萃分析仅使用来自公共数据库的研究水平数据，因此不需要正式的伦理批准。研究结果将以同行评议期刊的正式出版物或相关会议的报告形式发表。
PROSPERO 注册号：CRD42019118784。
