Can we improve prediction of Alzheimer's disease and Mild Cognitive Impairment by combining MMSE score and MRI-based imaging data?
Bibliographic record
Abstract
Abstract BACKGROUND This work aimed to find MRI-based markers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) to improve diagnosis. METHODS The multinomial logistic regression was used to predict diagnosis status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered as explanatory variables in the prediction model. RESULTS The best radiomic predictor was the relative brain volume defined as the ratio between the volume of the brain without cerebrospinal fluid and the volume of the whole brain multiplied by 100%. The model was trained on the ADNI dataset and tested on the independent EDSD dataset. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD vs NC for the EDSD. The comparison of two models: with the MMSE score only as an independent variable, and corrected for the relative brain value and age, shows that the addition of an MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% vs 71.08%) while maintaining quality for AD (AUC: 93.35% vs 93.25%). Additionally, among MCI patients predicted as AD inconsistently with original diagnosis, 56.25% from ADNI and 54.17% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. CONCLUSIONS The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports the AD/MCI diagnosis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".