ArticleJournal of dental anesthesia and pain medicine2026
Feasibility and preliminary validation of an artificial intelligence-based facial emotion recognition application for real-time assessment of pediatric dental anxiety.
Article in Journal of dental anesthesia and pain medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dental anxiety is prevalent among children worldwide and represents a barrier to pediatric dental care, contributing to care avoidance, treatment difficulty, and poorer oral health outcomes. Venham Clinical Anxiety Scale (VCAS) and Visual Facial Anxiety Scale (VFAS), are limited by subjectivity and interobserver variability. This study evaluated an artificial intelligence (AI)-based facial emotion recognition (FER) application for assessing dental anxiety across procedural stages in children undergoing tooth extraction. Methods: A total of 186 children aged 7-13 years who were undergoing dental extraction were enrolled. EmoDent, a SwiftUI iOS application using a convolutional neural network (CNN) model, captured facial expressions at four time points: pre-treatment, before local anesthesia, during extraction, and post-treatment. VCAS and VFAS scores were recorded. Spearman's rank correlation and Cohen's kappa were applied (P < 0.05). Results: Emotional responses varied significantly across treatment phases (P < 0.001). Happy (20.4%) and neutral (58.1%) expressions predominated pre-treatment, shifting to anger (49.5%) and sadness (24.7%) during local anesthesia, with partial recovery post-treatment. Concordance with traditional clinical scales was strongest during peak-stress phases. Although extreme trait prevalence mathematically deflated conventional Cohen's kappa (0.146 and -0.010 during local anesthesia), alternative metrics showed high monitoring agreement, with Gwet's AC1 reaching 0.941 versus VCAS and 0.942 versus VFAS. Conclusions: The application demonstrated valid and reliable emotional assessment across all procedural stages and showed strong concordance with conventional scales. It offers a promising non-invasive adjunct, particularly for non-verbal children. Future studies should incorporate multimodal data and clinician feedback.
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.