ArticleMedical image analysis2021
Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05366114 (Assessing the Feasibility of a Smartphone-based, Machine Learning Visual Imaging Application for Assessment of Hyperextensibility of Peripheral Joints in Ehlers Danlos Syndrome), which is not on this map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Assessing the Feasibility of a Smartphone-based, Machine Learning Visual Imaging Application for Assessment of Hyperextensibility of Peripheral Joints in Ehlers Danlos Syndrome
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
- Computer Vision Technologies in Movement Disorders: A Systematic Review.Movement disorders clinical practice · 2025Pooled it
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Automatic and explainable assessment for Parkinson's disease by video-based human motion understanding.Journal of neuroengineering and rehabilitation · 2026Article
- Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Deep learning-enabled accurate assessment of gait impairments in Parkinson's disease using smartphone videos.NPJ digital medicine · 2025Article
- Predictive modeling of motor symptom severity and stage classification in Parkinson's disease using machine learning methods with selected multiple serological biomarkers.BMC neurology · 2025Article
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases.NEJM AI · 2025Article
- [Research progress in motor assessment of neurodegenerative diseases driven by motion capture data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- A Graph-Theoretic Approach to Detection of Parkinsonian Freezing of Gait From Videos.Statistics in medicine · 2025Article
- GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease.Graphs in biomedical image analysis : 6th International Workshop, GRAIL 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. GRAIL (Workshop) (6th : 2024 : Marrakesh, Morocco) · 2025Article
- Tremor Severity and Operative Parameters Predict Imbalance in Patients Undergoing Focused Ultrasound Thalamotomy.Movement disorders clinical practice · 2024Article
- Data-Driven Discovery of Movement-Linked Heterogeneity in Neurodegenerative Diseases.Nature machine intelligence · 2024Article
- Deep Brain Stimulation restores information processing in parkinsonian cortical networks.medRxiv : the preprint server for health sciences · 2024Article
- Assisted technology in Parkinson's disease gait: what's up?Arquivos de neuro-psiquiatria · 2024Review
- Pose-based tremor type and level analysis for Parkinson's disease from video.International journal of computer assisted radiology and surgery · 2024Article
- Computer Vision for Parkinson's Disease Evaluation: A Survey on Finger Tapping.Healthcare (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
Parkinson's disease (PD) is a brain disorder that primarily affects motor function, leading to slow movement, tremor, and stiffness, as well as postural instability and difficulty with walking/balance. The severity of PD motor impairments is clinically assessed by part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a universally-accepted rating scale. However, experts often disagree on the exact scoring of individuals. In the presence of label noise, training a machine learning model using only scores from a single rater may introduce bias, while training models with multiple noisy ratings is a challenging task due to the inter-rater variabilities. In this paper, we introduce an ordinal focal neural network to estimate the MDS-UPDRS scores from input videos, to leverage the ordinal nature of MDS-UPDRS scores and combat class imbalance. To handle multiple noisy labels per exam, the training of the network is regularized via rater confusion estimation (RCE), which encodes the rating habits and skills of raters via a confusion matrix. We apply our pipeline to estimate MDS-UPDRS test scores from their video recordings including gait (with multiple Raters, R=3) and finger tapping scores (single rater). On a sizable clinical dataset for the gait test (N=55), we obtained a classification accuracy of 72% with majority vote as ground-truth, and an accuracy of ∼84% of our model predicting at least one of the raters' scores. Our work demonstrates how computer-assisted technologies can be used to track patients and their motor impairments, even when there is uncertainty in the clinical ratings. The latest version of the code will be available at https://github.com/mlu355/PD-Motor-Severity-Estimation.
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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.