ArticleScientific reports2024
Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating the Utility of Wearable Sensors for the Early Diagnosis of Parkinson Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Revolutionizing stroke prediction: a systematic review of AI-powered wearable technologies for early detection of stroke.Neurosurgical review · 2025Pooled it
- Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.Scientific reports · 2026Article
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- Frequency-based deep learning to identify subtle postural instability in early, untreated Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Machine learning classification of early-stage Parkinson's disease using sit-to-walk biomechanical features.Scientific reports · 2026Article
- Comparing the Informative Value of 2-Minute Segments of the 6-Minute Walk Test: Insights into a Prospective Study on Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Multidimensional factors of health-related quality of life in parkinson's disease using ensemble learning and network analysis.Scientific reports · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- CRISP: a correlation-filtered recursive feature elimination and integration of SMOTE pipeline for gait-based Parkinson's disease screening.Frontiers in computational neuroscience · 2025Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neural network (CNN) technique based on the 6-min walk test (6MWT) measured using wearable sensors to distinguish patients with early-stage PD (n = 78) from healthy controls (n = 50). The participants wore six sensors, and performed the 6MWT. The time-series data were converted into new images. The results revealed that the gyroscopic vertical component of the lumbar spine displayed the highest classification accuracy of 83.5%, followed by those of the thoracic spine (83.1%) and right thigh (79.5%) segment. These findings suggest that the 6MWT and CNN models may facilitate earlier diagnosis and monitoring of PD symptoms, enabling clinicians to provide timely treatment during the critical transition from normal to pathologic gait patterns.
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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.