Neural networks for cognitive testing: Cognitive test drawing classification
Bibliographic record
Abstract
With the burgeoning population of patients with cognitive disorders such as dementia, healthcare is already having significant difficulty in caring for this new population of patients. However, with the last decade's advances in neural networks, it is possible to begin creating software which may aid in healthcare for these patients. Specifically, it may aid in diagnosis, such as in expediting cognitive examinations. Within this paper, we describe a custom neural network utilizing a SqueezeNet which is used to classify a custom dataset of hand-drawn images commonly used in cognitive examinations. We demonstrate that our model has 97% accuracy. Specifically, this enables the development of entire automated and accurate cognitive examinations. The work presented here demonstrates neural networks may assist with healthcare for patients with cognitive disorders, having impact upon the fields of neurology, psychiatry, and family medicine. Importantly, within the context of the COVID-19 pandemic restricting in-person visits and promoting telemedicine, this provides the foundations to transition cognitive examinations to a telemedicine modality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".