Machine learning methods for automatic pain assessment using facial expression information: Protocol for a systematic...

Area of Science:

• Computational medicine and computer science.
• Interdisciplinary research in artificial intelligence and healthcare.

Background:

• Machine learning (ML) for pain prediction from facial expressions is an emerging field.
• Studies are often published in computer science journals, creating a knowledge gap for medical professionals.
• Lack of clinical utility discussion and adherence to reporting guidelines (e.g., TRIPOD) in some ML prediction papers.

Purpose of the Study:

• To systematically review and summarize evidence on the performance and utility of ML methods for automatic pain assessment via facial expressions.
• To address the knowledge gap and foster collaboration between computer scientists and medical researchers.
• To provide a comprehensive overview for clinicians and researchers interested in ML-based pain assessment.

Main Methods:

• Systematic literature search across PubMed, Web of Science, and IEEE Xplore databases.
• Conducting a systematic review and meta-analysis to evaluate ML methods.
• Performing subgroup analyses based on ML method types.

Main Results:

• The review will summarize accuracy, interpretability, generalizability, and computational efficiency of various ML methods.
• Meta-analysis will provide quantitative insights into the performance of different algorithms.
• Subgroup analyses will highlight strengths and weaknesses of specific ML approaches.

Conclusions:

• This study will provide a consolidated understanding of ML applications in facial pain assessment.
• Findings aim to guide the development and clinical implementation of reliable ML-based pain prediction tools.
• Promoting interdisciplinary collaboration for advancing pain management through AI.