A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning
Bibliographic record
Abstract
Alzheimer's disease (AD), an incurable disease, poses a major health problem. It is important to identify patients with mild cognitive impairment (MCI) and early AD. Clock rendering test (CDT) is an effect way to screen AD patients quickly in the community. However, the current CDT methods require specific equipment to collect features, and the existing prediction models are inefficient in early warning of MCI. To solve the problem, this paper replaces digital pen with fingertip interaction, and proposes an early warning model for AD early dCDT images based on ResNet50. The dCDT tests were carried out on normal cognitive elderly, MCI patients and mild AD patients, and the results were used to verify the analysis and classification ability of the ResNet50-based early AD prediction model, in contrast to the clock score-based early AD prediction model. The comparison shows that the ResNet50-based early AD prediction model is efficient in early warning than the other model, and is suitable for large-scale screening of AD patients in the community, in the absence of doctors.
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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 | 0.001 |
| 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".