ArticleFrontiers in neurology2024
Gait analysis in the early stage of Parkinson's disease with a machine learning approach.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Deep Learning-Based Parkinson's Disease Classification Using RGB Plantar Pressure Gait Images: A Comparative Study of CNN and Transformer Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative Analysis of Timed Up and Go Metrics Across Parkinson's Disease Severity and Their Clinical Correlations.Diagnostics (Basel, Switzerland) · 2026Article
- Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.Scientific reports · 2026Article
- Parkinson's disease focused multimodal wearable vision based gait monitoring system with real time feedback to support mobility and caregiver well-being.Discover mental health · 2026Article
- Attentive Prototype Learning with Wearable Sensor Mutual Information for Fall Risk Stratification of Parkinson's Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Deep Convolutional Neural Network-Based Detection of Gait Abnormalities in Parkinson's Disease Using Fewer Plantar Sensors in a Smart Insole.Biosensors · 2026Article
- Cross-sectional stratification of fall-related vulnerability in Parkinson's disease through multimodal fusion of speech, gait, facial expression, and upper-limb movement.Frontiers in digital health · 2026Article
- Deep learning-enabled accurate assessment of gait impairments in Parkinson's disease using smartphone videos.NPJ digital medicine · 2025Article
- Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Biological Nanotherapeutics Derived From Human Umbilical Cord Mesenchymal Stem Cells: Mechanisms and Translational Potential in Multisystem Therapies for Regeneration and Oncology.International journal of nanomedicine · 2025Review
- Mediation analysis for integrating motor symptoms of Parkinson's disease, cognitive function, and gait characteristics.Frontiers in neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Gait disorder is a prominent motor symptom in Parkinson's disease (PD), objective and quantitative assessment of gait is essential for diagnosing and treating PD, particularly in its early stage. Methods: This study utilized a non-contact gait assessment system to investigate gait characteristics between individuals with PD and healthy controls, with a focus on early-stage PD. Additionally, we trained two machine learning models to differentiate early-stage PD patients from controls and to predict MDS-UPDRS III score. Results: Early-stage PD patients demonstrated reduced stride length, decreased gait speed, slower stride and swing speeds, extended turning time, and reduced cadence compared to controls. Our model, after an integrated analysis of gait parameters, accurately identified early-stage PD patients. Moreover, the model indicated that gait parameters could predict the MDS-UPDRS III score using a machine learning regression approach. Conclusion: The non-contact gait assessment system facilitates the objective and quantitative evaluation of gait disorder in PD patients, effectively distinguishing those in the early stage from healthy individuals. The system holds significant potential for the early detection of PD. It also harnesses gait parameters for a reasoned prediction of the MDS-UPDRS III score, thereby quantifying disease severity. Overall, gait assessment is a valuable method for the early identification and ongoing monitoring of PD.
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.