Structural‐MRI‐based Alzheimer's disease dementia score using 3D convolutional neural networks to achieve accurate early disease prediction
Bibliographic record
Abstract
Abstract Background In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's Disease (AD). Due to memory constraints, many of the proposed CNNs work at a 2D slice‐level or 3D patch‐level. Other subject‐level 3D CNNs which take a whole brain 3D MRI image as input require a long training time. Method Here, we propose a lightweight subject‐level 3D CNN featuring dilated convolutions which allow the receptive field to be increased efficiently through a small number of layers. To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other databases/sites (AIBL), images acquired using different protocols (OASIS) and longitudinal images acquired over a short period of time (MIRIAD). Result We trained our network on the ADNI data, and we achieved a 5‐fold cross‐validated balanced accuracy of 88% in differentiating stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). Our network showed 78.5% accuracy in classifying images of mild cognitive impairment (MCI) subjects acquired 2 years prior to conversion to DAT. We achieved an overall specificity of 79.5% and sensitivity 79.7% on the entire set of 7902 independent test images Conclusion In this study, we constructed a lightweight 3D CNN network that converts the subject‐level image into a single AD dementia score to represent the disease progression. For estimating the generalization ability of the network to unseen data, independent testing is essential but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. The result of our study highlights the competitive performance of our network and potential promise for generalization.
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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 | 0.001 | 0.000 |
| 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".