ArticleJournal of Parkinson's disease2025
Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.
Article in Journal of Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Video-based machine learning models for predicting deep brain stimulation outcomes in Parkinson's disease patients.NPJ Parkinson's disease · 2026Article
- Diagnostic Accuracy of DaTQUANTBiomedicines · 2025Article
- AI-based retrospective analysis: differential improvement profiles of medication and deep brain stimulation in Parkinson's disease.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BackgroundClinical assessments of motor symptoms rely on observations and subjective judgments against standardized scales, leading to variability due to confounders. Improving inter-rater agreement is essential for effective disease management.ObjectiveWe developed an objective rating system for Parkinson's disease (PD) that integrates computer vision (CV) and machine learning to correct potential discrepancies among raters while providing the basis for model performance to gain professional acceptance.MethodsA prospective PD cohort (n = 128) were recruited from multi-centers. Motor examination videos were recorded using an android tablet with CV-based software following the MDS-UPDRS Part-III instructions. Videos included facial, upper- and lower-limb movements, arising from a chair, standing, and walking. Fifteen certified clinicians were recruited from multi-centers. For each video, five clinicians were randomly selected to independently rate the severity of motor symptoms, validate the videos and movement variables (MovVars). Machine learning algorithms were applied for automated rating and feature importance analysis. Inter-rater agreement among human raters and the agreement between artificial intelligence (AI)-generated ratings and expert consensus were calculated.ResultsFor all validated videos (n = 1024), AI-based ratings showed an average absolute accuracy of 69.63% and an average acceptable accuracy of 98.78% against the clinician consensus. The mean absolute error between the AI-based scores and clinician consensus was 0.32, outperforming the inter-rater variability (0.65), potentially due to the combined utilization of diverse MovVars.ConclusionsThe algorithm enabled accurate video-based evaluation of mild motor symptom severity. AI-assisted assessment improved the inter-rater agreement, demonstrating the practical value of CV-based tools in screening, diagnosing, and treating movement disorders.
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.