P4‐207: EMPLOYING ARTIFICIAL INTELLIGENCE IN THE DEVELOPMENT OF A SELF‐ADMINISTERED, COMPUTERISED COGNITIVE ASSESSMENT FOR THE ASSESSMENT OF NEURODEGENERATION
Bibliographic record
Abstract
We developed the Integrated Cognitive Assessment (ICA), a novel, computerised cognitive assessment test that aims at screening for cognitive impairment in a way that can simplify and accelerate that diagnosis of Alzheimer's Dementia (AD) and Mild Cognitive Impairment (MCI) .The test is self-administered, takes approximately five minutes and primarily focuses on measuring speed of information processing and utilises artificial intelligence in order to improve its predictive power.The ICA is independent of language and education and is free from learning bias (i.e. practice effect). We carried a head-to-head comparison of the ICA against the Montreal Cognitive Assessment (MoCA) and the Addenbrooke's Cognitive Assessment (ACE-III) in 69 participants of which 19 had a diagnosis of Mild AD, 21 had a diagnosis of MCI and 29 healthy volunteers where participants' age range varied from 50 to 83 years (mean= 67.1 years; Standard Deviation= 7.7 years). ICA's AI component (AI-engine) utilised a logistic regression model and yielded an Area-under-the-Receiver-Operating-Characteristic-Curve (AUC) score of 81.7% when discriminating between detection of cognitive impairment in the MCI and AD groups and the healthy volunteers group. The AUC for MoCA was at 73.5% and for the ACE-III at 73.9%.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".