Image-based machine learning model as a tool for classification of [ 18 F]PR04.MZ PET images in patients with parkinsonian syndrome
Bibliographic record
Abstract
Abstract Parkinsonian syndrome (PS) is characterized by bradykinesia, resting tremor, and rigidity, and it represents the phenotype observed in various neurodegenerative disorders. Positron emission tomography (PET) imaging plays an important role in diagnosing PS by detecting the progressive loss of dopaminergic neurons. This study aimed to develop and compare five machine-learning models for classifying [18F]PR04.MZ PET images between patients with PS and subjects without evidence for dopaminergic deficit (SWEDD). A dataset of [18F]PR04.MZ PET images from 204 subjects was analyzed and classified into PS compatible (1) and SWEDDs (0) by three blinded expert readers. The images were preprocessed to generate two and three-dimensional datasets. Five different pattern recognition algorithms, commonly used for image analysis, were trained and validated, comparing their performance to the majority reading of expert diagnosis considered as the standard of truth. Three models outperformed the others, achieving an accuracy greater than 98%. The results demonstrated that our machine-learning models, combined with [18F]PR04.MZ PET images, provide highly accurate and precise tools to support clinicians in PET image analysis. This approach may reduce the time required for interpretation and increase certainty in the diagnostic process.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".