The identification of cognitive impairment in Parkinson’s disease using biofluids, neuroimaging, and artificial intelligence
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) is a neurodegenerative movement disorder causing severe disability and cognitive impairment as the disease progresses. It is necessary to develop biomarkers for cognitive decline in PD for earlier detection and prediction of disease progression. Methods: We reviewed literature which used artificial intelligence-based techniques, which can be more sensitive than other analyses, to determine potential biomarkers for cognitive impairment in PD. Results: We found that combining biomarker types, including those from neuroimaging and biofluids, resulted in higher accuracy. Focused analysis on each biomarker type revealed that using structural and functional magnetic resonance imaging (MRI) resulted in accuracy and area under the curve (AUC) values above 80%/0.80, and that beta-amyloid-42 and tau were able to classify PD subjects by cognitive function with accuracy and AUC values above 90%/0.90. Discussion: We can conclude that applying both blood-based and imaging-based biomarkers may improve diagnostic accuracy and prediction of cognitive impairment in PD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".