ArticlePediatrics2015
Automated Assessment of Children's Postoperative Pain Using Computer Vision.
Article in Pediatrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- The Impact of Patient Interactive Systems on the Management of Pain in an Inpatient Hospital Setting: A Systematic Review.Applied clinical informatics · 2019Pooled it
- Automated Vision-Based Sensing of Paediatric Pain Using 2D-Facial Landmark Trajectories Derived from 3D Geometric Normalisation and a Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) Network.Sensors (Basel, Switzerland) · 2026Article
- Automatic pain assessment from facial action units in ICU patients via various machine learning models.Scientific reports · 2026Article
- Parental experience and professional training shape the perception of comfort and discomfort in preterm and term newborns.Scientific reports · 2026Article
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- Consensus, divergence, and challenges in pediatric pain management practices: a qualitative study from a multi-stakeholder perspective.BMC health services research · 2026Article
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Facial Reactivity to Emotional Stimuli is Related to Empathic Concern, Empathic Distress, and Depressive Symptoms in Social Work Students.Psychological reports · 2025Article
- Identification of technology-based models and efficacy of digital-based pain facial expression assessment tools among children: a systematic review.BMC nursing · 2025Article
- Review
- Using facial reaction analysis and machine learning to objectively assess the taste of medicines in children.PLOS digital health · 2024Article
- Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study.Korean journal of anesthesiology · 2024Article
- Severity and associated factors of postoperative pain in paediatric surgical patients aged 2 months-7 years at selected Addis Ababa Public Hospitals: a multicenter prospective longitudinal study.Annals of medicine and surgery (2012) · 2024Article
- Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.Journal of medical Internet research · 2024Article
- Review
- Using AI to Detect Pain through Facial Expressions: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Perioperative Care of Children with Severe Neurological Impairment and Neuromuscular Scoliosis-Journal of clinical medicine · 2022Review
- Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundCurrent pain assessment methods in youth are suboptimal and vulnerable to bias and underrecognition of clinical pain. Facial expressions are a sensitive, specific biomarker of the presence and severity of pain, and computer vision (CV) and machine-learning (ML) techniques enable reliable, valid measurement of pain-related facial expressions from video. We developed and evaluated a CVML approach to measure pain-related facial expressions for automated pain assessment in youth.
methodsA CVML-based model for assessment of pediatric postoperative pain was developed from videos of 50 neurotypical youth 5 to 18 years old in both endogenous/ongoing and exogenous/transient pain conditions after laparoscopic appendectomy. Model accuracy was assessed for self-reported pain ratings in children and time since surgery, and compared with by-proxy parent and nurse estimates of observed pain in youth.
resultsModel detection of pain versus no-pain demonstrated good-to-excellent accuracy (Area under the receiver operating characteristic curve 0.84-0.94) in both ongoing and transient pain conditions. Model detection of pain severity demonstrated moderate-to-strong correlations (r = 0.65-0.86 within; r = 0.47-0.61 across subjects) for both pain conditions. The model performed equivalently to nurses but not as well as parents in detecting pain versus no-pain conditions, but performed equivalently to parents in estimating pain severity. Nurses were more likely than the model to underestimate youth self-reported pain ratings. Demographic factors did not affect model performance.
conclusionsCVML pain assessment models derived from automatic facial expression measurements demonstrated good-to-excellent accuracy in binary pain classifications, strong correlations with patient self-reported pain ratings, and parent-equivalent estimation of children's pain levels over typical pain trajectories in youth after appendectomy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.