ArticleDiagnostics (Basel, Switzerland)2023
DeepSmile: Anomaly Detection Software for Facial Movement Assessment.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Telerehabilitation for peripheral facial palsy: A scoping review.Journal of telemedicine and telecare · 2026Article
- Objective Dynamic Assessment of Facial Movement Asymmetry in Children Using a Marker-Based Video Method.Journal of clinical medicine · 2026Article
- Surface Electromyographic Features for Severity Classification in Facial Palsy: Insights from a German Cohort and Implications for Future Biofeedback Use.Sensors (Basel, Switzerland) · 2025Article
- Interrater Reliability for Classifying Craniofacial Microsomia Severity: A Call for Objective Evaluation.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician's level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model's suggested healthy smile with the person's actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients' smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.
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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.