Early Detection of Alzheimer's Disease Using Deep Learning on Neuroimaging Data: a Review and Novel Approach
Bibliographic record
Abstract
Alzheimer's disease (AD) constitutes a major global health concern, with early detection being crucial for mitigating progression and enhancing patient outcomes. Traditional diagnostic approaches, although effective, are frequently invasive and time-intensive, underscoring the necessity for more efficient and non-invasive alternatives. Recent advancements in deep learning (DL) have facilitated the automation of neuroimaging data analysis, enabling earlier and more precise diagnoses. This paper examines the application of deep learning techniques—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models—in the detection of AD from MRI and PET scans. These models have exhibited strong performance in identifying subtle brain changes, achieving an accuracy of 98%, alongside other significant performance metrics such as sensitivity, specificity, and AUC (Area under the Curve), and further affirming their diagnostic potential. The review underscores recent advancements, identifies challenges such as data variability, and emphasizes the necessity for enhanced model interpretability. Furthermore, the integration of multi-modal neuroimaging data, along with genetic and biochemical markers, is discussed as a means to augment diagnostic precision. As deep learning models continue to advance, the amalgamation of large-scale, longitudinal datasets is anticipated to further revolutionize clinical practices, facilitating more personalized and effective strategies for the management of Alzheimer's disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".