ArticleBrain sciences2021
Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- A quantum-classical dual-track deep learning network for explainable Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Wearable-Sensor-Based Analysis of Aerial Archimedean Spirals for Early Detection of Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Clinical Applications and Measurement Properties of the Digitized Archimedes Spiral Drawing Test: A Scoping Review.Movement disorders clinical practice · 2025Article
- Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores inJournal of Parkinson's disease · 2025Article
- Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification.Scientific reports · 2025Article
- Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.Current medical science · 2025Article
- Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Low-cost and convenient screening of disease using analysis of physical measurements and recordings.PLOS digital health · 2024Review
- Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests.Scientific reports · 2024Article
- Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS).Diagnostics (Basel, Switzerland) · 2023Article
- Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional means of Parkinson's Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).
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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.