ArticleNPJ digital medicine2021
Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 18 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
18 citing papers in PubMed, 54 citations in OpenAlex.
- Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection.Sensors (Basel, Switzerland) · 2025Review
- Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system.Pituitary · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- Changes in facial expressions can distinguish Parkinson's disease via Bayesian inference.Frontiers in neurology · 2025Article
- Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease.Heliyon · 2023Article
- Facial expression of patients with Graves' orbitopathy.Journal of endocrinological investigation · 2023Article
- 3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema.PLOS digital health · 2023Article
- Article
- Does Father Christmas Have a Distinctive Facial Phenotype?Vision (Basel, Switzerland) · 2022Article
- An integrated biometric voice and facial features for early detection of Parkinson's disease.NPJ Parkinson's disease · 2022Article
- Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination.IEEE journal of biomedical and health informatics · 2022Article
- Automated video-based assessment of facial bradykinesia in de-novo Parkinson's disease.NPJ digital medicine · 2022Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- Watching Parkinson's disease with wrist-based sensors.NPJ digital medicine · 2022Article
- A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers.PLOS digital health · 2022Article
- Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach.Sensors (Basel, Switzerland) · 2022Article
- Analytical Validation of a Webcam-Based Assessment of Speech Kinematics: Digital Biomarker Evaluation following the V3 Framework.Digital biomarkersArticle
Corrections and comments
- Retraction · 2023-02-03Error in Analyses · Error in Data · Error in Methods ·
- Retracted
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
A prevalent symptom of Parkinson's disease (PD) is hypomimia - reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool ( www.parktest.net ). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.
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.