A Vision-Based Approach for the Automated Evaluation of the MoCA Clock-Drawing Test Using a YOLO Neural Network
Bibliographic record
Abstract
This paper presents an analysis of the clock-drawing test from the Montreal Cognitive Assessment (MoCA), using computer vision and deep learning techniques to perform an evaluation based on the Rouleau scale. The proposed system consists of 3 components: 1) computer vision techniques such as convex hull and morphological operations to evaluate the contour; 2) a combination of computer vision and the YOLO-v8 algorithm to assess the presence and correct positioning of numbers; and 3) an analysis of the hands using the Hough Line Transform. The methodology was evaluated using 29 samples from individuals with Parkinson's disease, and the results were benchmarked against assessments from two human evaluators, one of whom is a MoCA-certified examiner. The proposed approach achieved an inter-rater correlation of 0.915 with the certified examiner, highlighting the system's efficacy in analyzing clock-drawing tasks and potentially classifying cognitive impairment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".