Deep Learning Application for Monitoring Handwritten Spirals in Parkinson's Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder that affects the central nervous system and progressively impairs the control of voluntary movements. The growing body of published research in this field demonstrates the increasing attention that spiral analysis (SA) has attracted in studies focused on the diagnosis and monitoring of PD. SA-based detection methods have shown superior performance compared to traditional approaches involving clinical evaluations, laboratory tests, and neuroimaging, largely due to their practicality and cost-efficiency. This study investigates the potential of spiral drawing tasks as a tool for monitoring Parkinson’s disease by applying machine learning (ML) techniques to assess disease status. Specifically, four convolutional neural network (CNN) architectures—VGG16, ResNet50, MobileNetV2, and Xception—were employed to classify individuals into two groups—Parkinson’s Disease (PD) and Control—and to detect disease stages within the PD group based on the HoehnandYahrscaleacross multiple data collection sessions. Classification performance was assessed using accuracy and F1-score metrics. The results demonstrate that spiral-based assessments, combined with ML approaches, can effectively support both the diagnosis and longitudinal monitoring of Parkinson’s disease progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".