ArticleJournal of oral rehabilitation2022
Automated detection of smiles as discrete episodes.
Article in Journal of oral rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Association of Clinical, Biomechanical, and Psychosocial Factors with Smile Dynamics in Unilateral Cleft Lip: A Multicenter Observational Study.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Observational
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Smile analysis in dentistry and orthodontics - a review.Journal of the Royal Society of New Zealand · 2025Review
- Identification of smile events using automated facial expression recognition during the Autism Diagnostic Observation Schedule (ADOS-2): a proof-of-principle study.Frontiers in psychiatry · 2025Article
- Automated detection of smiles as discrete episodes.Journal of oral rehabilitation · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPatients seeking restorative and orthodontic treatment expect an improvement in their smiles and oral health-related quality of life. Nonetheless, the qualitative and quantitative characteristics of dynamic smiles are yet to be understood.
objectiveTo develop, validate, and introduce open-access software for automated analysis of smiles in terms of their frequency, genuineness, duration, and intensity. MATERIALS AND
methodsA software script was developed using the Facial Action Coding System (FACS) and artificial intelligence to assess activations of (1) cheek raiser, a marker of smile genuineness; (2) lip corner puller, a marker of smile intensity; and (3) perioral lip muscles, a marker of lips apart. Thirty study participants were asked to view a series of amusing videos. A full-face video was recorded using a webcam. The onset and cessation of smile episodes were identified by two examiners trained with FACS coding. A Receiver Operating Characteristic (ROC) curve was then used to assess detection accuracy and optimise thresholding. The videos of participants were then analysed off-line to automatedly assess the features of smiles.
resultsThe area under the ROC curve for smile detection was 0.94, with a sensitivity of 82.9% and a specificity of 89.7%. The software correctly identified 90.0% of smile episodes. While watching the amusing videos, study participants smiled 1.6 (±0.8) times per minute.
conclusionsFeatures of smiles such as frequency, duration, genuineness, and intensity can be automatedly assessed with an acceptable level of accuracy. The software can be used to investigate the impact of oral conditions and their rehabilitation on smiles.
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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.