AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer’s disease: a multicenter study
Bibliographic record
Abstract
Introduction In 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders. Methods We tested the performance of an artificial intelligence (AI) tool applied to neuropsychological and magnetic resonance imaging (MRI) assessment for staging and causal hypothesis, which are the two recommended workflow steps guiding the next one recommending optimal biomarkers to be used for a biological diagnosis of neurocognitive disorders, according to intersocietal recommendations. Moreover, we assessed the AI performance in predicting the progression to Alzheimer’s disease (AD)-dementia. Results For the three-class classification of staging (n patients = 426), the inter-rater AI-humans agreement was substantial for both healthy subjects/subjective cognitive impairment/worried-well vs. all the remaining groups (rest) (Cohen’s κ = 0.81) and mild cognitive impairment/mild dementia vs. rest κ = 0.70) classification, almost perfect for moderate/severe dementia vs. rest κ =0.90) classification. For the three-class classification of causal hypotheses ( n = 112), the AI performance vs. biomarker-based diagnosis was: positive predictive value 91% [95% CI: 84–96%]; negative predictive value 100%, and accuracy 91% [84–96%]. For the binary classification of progression or not progression to AD-dementia at 24-month, with clinical conversion as a reference standard ( n = 341), the AI performance was: sensitivity 89% [84–94%], specificity 82% [77–87%]; accuracy 85% [81–89%]; and area under the receiver operating characteristic curve 83% [79–87%]. Discussion The AI tool showed high agreement with human assessment for staging, high accuracy with biomarkers for causal hypotheses of neurocognitive disorders and predicted progression to AD at 24-month with 89% sensitivity and 82% specificity.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".