Parkinson's Disease Detection on DaTscan Images Using Random Forest and Graph Deep Learning
Bibliographic record
Abstract
Parkinson's disease is characterized by motor disorders caused by the progressive death of neurons producing dopamine. DaTscan imaging, namely Single-Photon Emission Computed Tomography (SPECT) with ioflupane (${ }^{123} I$), allows to quantify this loss of neurodopaminergic neurons, more particularly observed in the striatum. In this article, a two-step classification method is proposed to detect Parkinson's disease on DaTscan images focusing on this key structure. A graph is first created from the segmentation of the striatum, relying on the creation of the Max-Tree of the DaTscan image and a prior segmentation. Then, a Random Forest algorithm is applied to classify the nodes of the striatal graph, computing features extracted locally or related to subject's properties. Finally, the classification is made on the whole graph using Deep Learning, and adding the Random Forest predictions as new features. The combination of these two approaches offers promising results on the Parkinson's Progression Markers Initiative (PPMI) database, achieving an average balanced accuracy of$91.1\%$using a nested cross-validation strategy to increase the robustness of the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".