Predicting Alzheimer’s disease progression in healthy and MCI subjects using multi‐modal deep learning approach
Bibliographic record
Abstract
Abstract Background Alzheimer's disease (AD) is a complex disorder influenced by many factors, but it is unclear how each factor contributes to disease progression. An in‐depth examination of these factors may yield an accurate estimate of time‐to‐conversion to AD for patients at various disease stages. Recent advances in deep learning have enabled researchers to predict patient’s disease onset time by exploring the influencing factors in AD progression. Method We used 543 subjects with 63 features from 3 data modalities from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The following modalities were used: 1) MRI, 2) genetic and 3) DTC (Demographic, cognitive Tests and Cerebrospinal fluid). The 21 most important features were automatically selected for the three modalities. We used a Deep Learning‐based survival analysis model that extends the classic Cox regression model to predict the subjects' disease onset time. Here we re‐define the non‐AD‐progression as “survivor”, and AD‐progression as “non‐survivor”. The subjects were divided into two groups: progressive subjects (non‐survivor), who were either healthy or diagnosed with Mild Cognitive Impairment (MCI) at initial clinical visit and later developed AD, and non‐progressive subjects ("survivor”), who were either healthy or MCI at initial visit but did not develop AD later. We used 10 random sub‐samples, selecting 80% of the subjects for training and 20% for testing each time; 20% of training data was used for internal validation. Result Figure 1 shows the estimated survival rates over 10 years. Both groups had a high survival chance at the start. The progressive group's survival chance dropped much faster and fell below 20% by the end of the period. The non‐progressive group's survival chance remained around 50%. Feature importance analysis is displayed in Figure 2. Eight of the top ten most important features are from the cognitive tests, demonstrating their importance in survival analysis. Amygdala and Hippocampus regions, as well as age, are also notable features. Conclusion Our study demonstrated that using powerful predictive models on multi‐modal data can improve prediction of time‐to‐conversion. This not only leads to a better understanding of AD, but also provides essential tools for practitioners who wish to follow their patients' disease progression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".