ArticleFrontiers in medicine2022
Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- Facial Expression-based Pain Detection: Development and Validation in the Post-anesthesia Care Unit.Anesthesiology and pain medicine · 2026Article
- Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review.Journal of medical systems · 2026Review
- Observational
- Behavioral assessment of pain in rodents: advances from evoked responses to spontaneous states and multimodal approaches.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Automated pain detection using facial expression in adult patients with a customized spatial temporal attention long short-term memory (STA-LSTM) network.Scientific reports · 2025Article
- Pain Management in Cancer Patients With Artificial Intelligence: Narrative Review.Scientifica · 2025Review
- Applications of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2024Review
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Pain Research and Treatment: Policy Implications.Policy insights from the behavioral and brain sciences · 2024Article
- Calibration trial of an innovative medical device (Frontiers in medicine · 2024Article
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- Review
- Using AI to Detect Pain through Facial Expressions: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Using artificial intelligence to improve pain assessment and pain management: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Deep Learning-Based Grimace Scoring Is Comparable to Human Scoring in a Mouse Migraine Model.Journal of personalized medicine · 2022Article
- Predicting Do-Not-Resuscitate Decisions in Critically Ill Patients Through Using Multitask Learning: A Retrospective Study of the MIMIC-IV Database.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Pain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions. Methods: We enrolled critically ill patients during 2020-2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score. Results: A total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high. Conclusions: The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.
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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.