Validation of an Magnetic Resonance Imaging Acquisition and Review Protocol for Alzheimer's Disease and Related Disorders
Bibliographic record
Abstract
PURPOSE: Magnetic resonance imaging (MRI) of the brain allows for the identification of structural lesions typical of Alzheimer's disease (AD), the main cause of dementia. However, to have a clinical impact, it is imperative that acquisition and reporting of this MRI-based evidence be standardized, ensuring the highest possible reliability and reproducibility. Our objective was to validate a systematic radiological MRI acquisition and review process in the context of AD. METHODS: We included 100 individuals with a suspicion of dementia due to AD for whom MRI were acquired using our proposed protocol of clinically achievable acquisitions and used a unified reading grid to gather semi-quantitative evidence guiding diagnostic. MRIs were read by 3 raters with different experience levels. Interrater reliability was measured using Cohen's kappa statistic. RESULTS: Interrater reliability average for lesions occupying space, hemorrhage, or ischemia, was respectively 0.754, 0.715, and 0.501. Average reliability of white matter hyperintensity burden (Fazekas), global cortical atrophy, and temporal lobe atrophy (Scheltens) scales was 0.687, 0.473, and 0.621 (right)/0.599 (left), respectively. The kappas for regional cortical atrophy (frontal, parietal, occipital, temporal, and posterior cingulum) varied from 0.281-0.678. The average MRI reading time varied between 1.43-5.22 minutes. CONCLUSIONS: The presence of space occupying lesions, hemorrhagic or ischemic phenomena, and radiological scales have a good interrater reproducibility in MRI. Coupled with standardized acquisitions, such a protocol should be used when evaluating possible dementias, especially those due to probable AD.
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 | 0.474 | 0.480 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".