SynthesisMovement disorders clinical practice2025
Computer Vision Technologies in Movement Disorders: A Systematic Review.
Synthesis in Movement disorders clinical practice, 2025. 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.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Dystonia 3×3: A Training Guideline for Beginners in Botulinum Neurotoxin Treatment of Cervical Dystonia.Toxins · 2026Guideline
- 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 Analysis for Objective Motor Assessment in Parkinson's Disease: A Retrospective Study.Movement disorders clinical practice · 2026Article
- Automatic and explainable assessment for Parkinson's disease by video-based human motion understanding.Journal of neuroengineering and rehabilitation · 2026Article
- From RGB-D to RGB-Only: Reliability and Clinical Relevance of Markerless Skeletal Tracking for Postural Assessment in Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Automated video analysis for early detection of bradykinesia in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Abnormal head movements in neurological conditions: a knowledge-based dataset with application to cervical dystonia.Frontiers in digital health · 2026Article
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
- Modern Phenomenology of Tremor: How Novel Technology Can Assist in the Diagnosis of Tremor.Movement disorders clinical practice · 2025Article
- Overlap and Differences of Autism and ADHD: Digital Phenotyping of Movement and Communication During Development.bioRxiv : the preprint server for biology · 2025Article
- Tremor: Clinical Frameworks, Network Dysfunction and Therapeutics.Brain sciences · 2025Review
- Digital motor markers for early autism detection: promise, pitfalls, and a path to clinics.Frontiers in psychiatry · 2025Article
- Digital monitoring of motor function in Parkinson's disease using Markerless motion analysis and exergaming.Frontiers in neurologyArticle
- 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEvaluation of movement disorders primarily relies on phenomenology. Despite refinements in diagnostic criteria, the accuracy remains suboptimal. Such a gap may be bridged by machine learning and video technology, which permit objective, quantitative, non-invasive motor analysis. Markerless automated video-analysis, namely Computer Vision, emerged as best suited for ecologically-valid assessment.
objectivesTo systematically review the application of Computer Vision for assessment, diagnosis, and monitoring of movement disorders.
methodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched Cochrane, Embase, PubMed, and Scopus databases for articles published between 1984 and September 2024. We used the following search strategy: ("video analysis" OR "computer vision") AND ("Parkinson's disease" OR "PD" OR "tremor" OR "dystonia" OR "parkinsonism" OR "progressive supranuclear palsy" OR "PSP" OR "multiple system atrophy" OR "MSA" OR "corticobasal syndrome" OR "CBS" OR "chorea" OR "ballism" OR "myoclonus" OR "Tourette's syndrome").
resultsOut of 1099 identified studies, 61 met inclusion criteria, and 10 additional studies were included based on authors' judgment. Parkinson's disease was the most investigated movement disorder, with gait as the prevalent motor task. OpenPose was the most used pose estimation software. Automated video-analysis consistently achieved diagnostic accuracies exceeding 80% across most movement disorders. For tremor, dystonia severity and tic detection, Computer Vision strongly aligned with accelerometery and clinical assessments.
conclusionsComputer Vision holds potential to provide non-invasive quantification of presence and severity of movement disorders. Heterogeneity in video settings, software usage, and definition of standardized guidelines for videorecording are challenges to be addressed for real-word applications.
Indexed as
Identifiers
What Socratic 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 Socratic graph.