Artificial Intelligence and Machine Learning in Neurodegenerative Disease Diagnosis: Techniques, Data Modalities, Challenges, and Future Directions
Bibliographic record
Abstract
Neurodegenerative disorders (NDDs), i.e., Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are a severe global health concern because of their increasing prevalence and challenge in early and accurate diagnosis. The traditional diagnostic tools rely on subjective clinical opinion and diagnose the disease late and therefore exclude early intervention. In this review, we discuss the revolutionary promise of Artificial Intelligence (AI) and Machine Learning (ML) methods for enhancing the diagnosis of these disorders. We discuss different data modalities used, e.g., neuroimaging (MRI, PET), biological fluid markers (CSF, blood), electrophysiological signals (EEG, EMG), and clinical/behavioral data. We discuss the principal AI/ML methods, e.g., traditional machine learning schemes and deep learning methods, in the context of their being used for different neurodegenerative disorders. We discuss the challenges faced now, e.g., data availability, model interpretability, and generalizability, as well as the imperative ethical and regulatory concerns. Finally, this paper suggests possible future directions of research, e.g., multi-modal data fusion, explainable AI, federated learning, and personalized diagnostic strategies towards accelerating the translation of AI/ML innovations to clinical practice.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".