ArticleJournal of medical Internet research2020
Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Computer Vision Technologies in Movement Disorders: A Systematic Review.Movement disorders clinical practice · 2025Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Interaction- and asymmetry-aware facial blendshape analysis for objective quantification of Parkinsonian hypomimia.NPJ Parkinson's disease · 2026Article
- NeuroSift for Task-Aware Quality Assurance of Multimedia Data in Remote Parkinson Disease Assessment: Machine Learning Model Development and Validation Study.Journal of medical Internet research · 2026Article
- Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.JMIR formative research · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Validation of remote multimodal AI screening for Parkinson disease across diverse settings.Communications medicine · 2026Article
- Objective Assessment of Facial Expressions in Parkinson's Disease During Deep Brain Stimulation ON and OFF States: A Pilot Study.European journal of neurology · 2026Article
- Review
- Facial Expression Metrics as Digital Biomarkers of Neurologic Disease.Neurology open access · 2025Article
- Proceedings of the world summit on parkinson's disease.NPJ Parkinson's disease · 2025Article
- AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.Annals of neurology · 2025Review
- Explaining facial action units' correlation with hypomimia and clinical scores in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Automated identification of early to mid-stage Parkinson's disease using deep convolutional neural networks on static facial images.Frontiers in medical technology · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- FaceFinder: A machine learning tool for identification of facial images from heterogenous datasets.AJO international · 2024Article
- Digital biomarkers for precision diagnosis and monitoring in Parkinson's disease.NPJ digital medicine · 2024Review
- Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos.Expert systems with applications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD).
objectiveThis study proposes methods to diagnose PD through facial expression recognition.
methodsWe collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD.
resultsThe experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved.
conclusionsThis study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.
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.