Assessment of cognitive function using Altoida DNS: Clinical reliability and comparison with Mini-Mental State Examination and Montreal Cognitive Assessment
Bibliographic record
Abstract
Introduction: The Altoida DNS application enables patients to assess cognitive and functional abilities through motor tasks and augmented reality powered by artificial intelligence, in a natural and engaging way. It allows early, accurate, fast, personalized, and non-invasive detection of cognitive decline. This study analyzes the outcomes obtained with Altoida, evaluates its clinical reliability, and compares it with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Methods: This is an observational, retrospective, and analytical study using 60 previously recorded medical records and cognitive data from the Altoida application. Variables included sex, age, cognitive complaints, and diagnostic hypotheses. Results: Among the evaluated patients, the most sensitive cognitive domains with the lowest average scores (classified as Low or Low Average) were planning, spatial memory, complex attention, cognitive processing speed, and perceptual motor coordination. These results were supported by findings from conventional neuropsychological tests. Discussion: The Altoida DNS assesses Mild Cognitive Impairment across various domains: Perceptual Motor Coordination, Complex Attention, Cognitive Processing Speed, Inhibition, Flexibility, Visual Perception, Planning, Prospective Memory, and Spatial Memory. Scores are classified into Low, Low Average, Average, or High Average. The first two indicate cognitive vulnerability or decline. Its strong correlation with MoCA and MMSE supports its validity. Conclusion: Due to its accessibility and objectivity, Altoida DNS may enable earlier diagnosis, faster patient inclusion in clinical trials, and more sensitive monitoring of cognitive decline and treatment response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".