Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis. Issue 12 (11th December 2019)

• Record Type: Journal Article
• Authors: Cheng, Dan; Liu, Dianbo; Philpotts, Lisa Liang; Turner, Dana P; Houle, Timothy T; Chen, Lucy; Zhang, Miaomiao; Yang, Jianjun; Zhang, Wei; Deng, Hao
• Is Part Of: BMJ open. Volume 9:Issue 12(2019)
• Publication Date: 2019-12-11
• Subjects: infant -- pain -- facial expression -- machine learning -- artificial intelligence; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-030482
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: 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. Methods and analysis: 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. Ethics and dissemination: 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. PROSPERO registration number: CRD42019118784.