Alzheimer Screening using Drawing Test Scores
Bibliographic record
Abstract
Screening for Alzheimer's disease is an important step in the effort to detect and inhibit the progress of Alzheimer's disease. The purpose of this research is to develop an application for a clock drawing test which is often seen as a part of the Alzheimer's screening tests such as the Montreal Cognitive Assessment (MoCA) test and the Clock Drawing Test (CDT). For our clock drawing testing, the full score is three points. The first point is from drawing the rounded contour. The second point is from drawing the clock numbers in the correct order. The third point is from drawing the clock hands correctly according to the test command. For the contour drawing and the clock hands drawing, a series of Image Processing techniques are used to check the roundness property of the contour and the shape of the clock hands. MNIST Classifier Model is used to detect the clock numbers and rule-based checking is then employed to give the score. During the evaluation, the application gives a score between 0 and 3 points, and this score is added up with the rest of the application. The results from 50 low-risk people showed that 74 percent of them received 3 points, 22 percent received 2 points and 4 percent received only 1 point.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| 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) | 0.009 | 0.003 |
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".