ArticleJournal of medical Internet research2021
Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 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
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Remote clinical decision support tool for Parkinson's disease assessment using a novel approach that combines AI and clinical knowledge.BMC medical informatics and decision making · 2025Article
- Automatic analysis of eyelid movement in de-novo Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Dual stream transformer for medication state classification in Parkinson's disease patients using facial videos.NPJ digital medicine · 2025Article
- Explaining facial action units' correlation with hypomimia and clinical scores in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson's Disease.Brain sciences · 2024Review
- Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.Movement disorders : official journal of the Movement Disorder Society · 2024Article
- Emotion expressions and cognitive impairments in the elderly: review of the contactless detection approach.Frontiers in digital health · 2024Review
- Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease.Heliyon · 2023Article
- Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care.Phenomics (Cham, Switzerland) · 2022Review
- Automated video-based assessment of facial bradykinesia in de-novo Parkinson's disease.NPJ digital medicine · 2022Article
- Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development.JMIR mental health · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFacial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to "hypomimia" or "masked facies."
objectiveWe aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD.
methodsWe trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD.
resultsThe algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda's seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7).
conclusionsThis proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient's motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.
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