P.012 SketchNet: Equipping Cognitive Examinations With Neural Network Computer Vision
Bibliographic record
Abstract
Background: With the advance of technology, our capacity to assess patients with dementia is also developing. It is possible to administer cognitive examinations using technology, such as the iPad-based Toronto Cognitive Assessment, but hitherto difficult to autonomously administer them. Many of the ’inputs’ from patients could be easily scored with software, but highly variable inputs such as the clock drawing are extremely difficult to score, precluding automated administration and scoring. This work focuses on the development of a neural network designed to assess cube drawings, infinity drawings, and clock drawings. Methods: 3200 drawings, evenly split between clocks, cubes and infinities were generated, with half being correct and half incorrect. A SqueezeNet was trained on 2000 images, validated on 800 drawings, and then tested on 400 drawings. Results: The SqueezeNet was able to achieve 97% accuracy on 400 images it had never seen before in categorizing images as “Cube”, “Clock”, “Infinity”, or “Other” (incorrectly drawn). Conclusions: This neural network can successfully determine the difference between correctly and incorrectly drawn images commonly used in cognitive examinations, overcoming the final barrier to autonomously administering and scoring cognitive examinations. Next steps are to clinically validate an autonomous examination program which has been modeled after the Addenbrooke Cognitive Examination-3.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".