A remote digital tool for diagnosis and monitoring of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. Method We developed the Integrated Cognitive Assessment (ICA); a 5 minute computerised cognitive test employs Artificial Intelligence (AI) to improve its accuracy in detecting cognitive impairment. ICA presents a series of rapidly changing images on a mobile device to measure cognitive impairment via a person's accuracy and response time in categorising those images. We studied the ICA in a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, the Montreal Cognitive Assessment (MoCA) and Addenbrooke’s Cognitive Examination (ACE) cognitive tests. Result The ICA demonstrated convergent validity with MoCA (r=0.58) and ACE (r=0.62). The ICA AI model was able to detect cognitive impairment with an area under the curve of 81% for MCI patients (MoCA 77%), and 88% for mild AD patients (MoCA 89%). The AI classifier, based on an explainable logistic regression model, demonstrated improved performance with increased training data. Furthermore it showed generalisability in performance from one population to another. The ICA was able to detect cognitive impairment with high accuracy when trained with one cohort and tested in an independent cohort with different cultural and demographic characteristics, a prerequisite for large population deployment. In a monitoring study, 12 healthy participants self‐administered 78 ICA tests remotely over a period of 3 months (936 tests in total). The ICA demonstrated no significant practice effect observed over the duration of the study. Conclusion The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large‐scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture and education and has additional advantages over standard of care tests because of its shorter duration, automatic scoring and potential for medical record or research database integration. The pandemic has presented a challenge for face‐face assessments. A digital tool such as the ICA allows us to adapt to these changes by administering assessments remotely and monitoring disease progression.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".