ArticleJournal of Parkinson's disease2022
Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson's Disease.
Article in Journal of Parkinson's disease, 2022. 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, 31 citations in OpenAlex.
- Computer Vision Technologies in Movement Disorders: A Systematic Review.Movement disorders clinical practice · 2025Pooled it
- Use of Smartphones and Wrist-Worn Devices for Motor Symptoms in Parkinson's Disease: A Systematic Review of Commercially Available Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- 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
- Three-Dimensional Point-Cloud Learning for Patient-Specific Deep Brain Stimulation Motor Response Prediction in Parkinson Disease.BME frontiers · 2026Article
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.Journal of Parkinson's disease · 2025Article
- Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson's Disease.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Brain Stimulation restores information processing in parkinsonian cortical networks.medRxiv : the preprint server for health sciences · 2024Article
- Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation.NPJ digital medicine · 2024Article
- Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- A Computer Method for Pronation-Supination Assessment in Parkinson's Disease Based on Latent Space Representations of Biomechanical Indicators.Bioengineering (Basel, Switzerland) · 2023Article
- Association of 7-Day Profiles of Motor Activity in Marital Dyads with One Component Affected by Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Anatomical substrates and connectivity for bradykinesia motor features in Parkinson's disease after subthalamic nucleus deep brain stimulation.Brain communications · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmong motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging.
objectiveUsing a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III.
methodsWe adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital.
resultsWe developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines.
conclusionWe developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.
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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.