ArticleScientific reports2025
Construction and validation of a pain facial expressions dataset for critically ill children.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Impact of a Visualization Training Platform on PICU Nurses' Pain Assessment Skill in Critically Ill Children: A Mixed-Methods Randomized Controlled Trial.Nursing open · 2026Trial
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- The effect of human ratings reliability on machine learning model performance: a case study in infant pain assessment.Scientific reports · 2026Article
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
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Authors and funding
11 authors.
Funding
Abstract
Automatic pain assessment for non-communicative children is in high demand. However, the availability of related training datasets remains limited. This study focuses on creating a large-scale dataset of pain facial expressions specifically for Chinese critically ill children and evaluating its utility using deep learning models. Data were gathered from two intensive care units at Children's Hospital of Fudan University. The dataset, named pain facial expression of critically ill children (PFECIC), includes 119 pain expression videos and 6951 images collected from 53 children between December 2022 and January 2023. All videos and images were independently triple labeled according to five pain levels. The PFECIC dataset was evaluated through deep learning experiments, demonstrating strong performance metrics: 88.3% accuracy, 88.3% precision, 88.7% recall, an F1-score of 88.5%, and a false-positive rate of 3.0%. Prediction errors were mostly associated with labels close to the true values. Comparative analysis with the classification of pain expressions (COPE) dataset highlighted the superiority of PFECIC in terms of accuracy, validity, and comprehensiveness.
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