ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2024
Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 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
- Tracking the longitudinal progression of Bradykinesia in Parkinson's disease from videos using VisionMD.Journal of Parkinson's disease · 2026Article
- Computer Vision Analysis for Objective Motor Assessment in Parkinson's Disease: A Retrospective Study.Movement disorders clinical practice · 2026Article
- Modified graph neural network-oriented optimization model for the classification of PD.Scientific reports · 2026Article
- Distinct finger-tapping feature in progressive supranuclear palsy correlates with motor function and brain atrophy.NPJ Parkinson's disease · 2026Article
- Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Automated video analysis for early detection of bradykinesia in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Explainable multimodal feature fusion networks for Parkinson's disease prediction.Frontiers in digital health · 2026Article
- Task-specific machine learning system for automatic assessment of multi-limb bradykinesia in Parkinson's disease.Frontiers in neuroscience · 2026Article
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Video analysis reveals early signs of Bradykinesia in REM sleep behavior disorder and Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Medication versus globus pallidus internus deep brain stimulation in Parkinson's disease with deep learning video analysis of finger tapping.Scientific reports · 2025Observational
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- VisionMD: an open-source tool for video-based analysis of motor function in movement disorders.NPJ Parkinson's disease · 2025Article
- What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.Parkinsonism & related disorders · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
introductionParkinson's disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Despite its reliability, the scale is bounded by a 5-point scale that limits its ability to track subtle changes in disease progression and is prone to subjective interpretations. We aimed to develop an automated system to objectively quantify motor symptoms in PD using Machine Learning (ML) algorithms to analyze videos and capture nuanced features of disease progression.
methodsWe analyzed videos of the Finger Tapping test, a component of the MDS-UPDRS, from 24 healthy controls and 66 PD patients using ML algorithms for hand pose estimation. We computed multiple movement features related to bradykinesia from videos and employed a novel tiered classification approach to predict disease severity that employed different features according to severity. We compared our video-based disease severity prediction approach against other approaches recently introduced in the literature.
resultsTraditional kinematics features such as amplitude and velocity changed linearly with disease severity, while other non-traditional features displayed non-linear trends. The proposed disease severity prediction approach demonstrated superior accuracy in detecting PD and distinguishing between different levels of disease severity when compared to existing approaches.
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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.