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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
  1. Dan Cheng1,2,
  2. Dianbo Liu3,
  3. Lisa Liang Philpotts4,
  4. Dana P Turner2,
  5. Timothy T Houle2,
  6. Lucy Chen2,
  7. Miaomiao Zhang5,
  8. Jianjun Yang1,
  9. Wei Zhang1,
  10. Hao Deng2,6
  1. 1 Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
  2. 2 Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
  3. 3 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  4. 4 Treadwell Library, Massachusetts General Hospital, Boston, Massachusetts, USA
  5. 5 Department of Engineering, University of Virginia, Charlottesville, Virginia, USA
  6. 6 DRPH Program, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
  1. Correspondence to Dr Hao Deng; hdeng1{at}; Professor Wei Zhang; zhangw571012{at}


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.

  • infant
  • pain
  • facial expression
  • machine learning
  • artificial intelligence

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  • Contributors HD, DC and DL contributed to the conception and design of the study. The manuscript protocol was drafted by DC and was revised by DPT, TTH, LC, MZ, JY, WZ and HD. The search strategy was developed by LLP, HD, DC, and revised by the other authors. Search strategy will be performed by DC and DL, who will also independently screen the potential studies, extract data from the included studies, assess the risk of bias and complete the data synthesis. HD and WZ will arbitrate in cases of disagreement and ensure the absence of errors. All authors approved the publication of the protocol.

  • Funding This work is supported by the National Natural Science Foundation of China, with the funding reference number of 81571082. This project is also supported by the Youth Creative Fund of The First Affiliated Hospital of Zhengzhou University.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval This systematic review and meta-analysis will only use study-level data from public databases, thus formal ethical approval is not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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