An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study
Bibliographic record
Abstract
Objective: To develop Digital Processing Speed Test (DPST), a free, automated, multilingual, artificial intelligence-based cognitive testing application, with the aim to enhance recognition of cognitive impairment in underserved communities by leveraging mobile health to improve cognitive testing's accessibility. Patients and Methods: In this cross-sectional feasibility and diagnostic study, we determined the test performance of DPST for the identification of mild cognitive impairment (MCI) and dementia, compared with traditional cognitive tests, such as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study was conducted from January 19, 2021, to November 12, 2023. In total, 476 adult participants were recruited by consecutive sampling at waiting areas of primary and secondary care clinics. The participants completed MMSE and MoCA with trained assessors and then performed DPST independently on a mobile device. The reference standard was a clinical diagnosis of MCI/dementia by a memory specialist blinded to the DPST score. Results: Area under the receiver operating characteristic curve analyses showed that area under the curves were similar for the 3 tests (MMSE, 0.862; MoCA, 0.888; DPST, 0.861). Likewise, sensitivity (DPST, 85.2%; MMSE, 85.2%; MoCA, 90.2%), negative likelihood ratio (DPST, 0.197; MMSE, 0.193; MoCA, 0.129), specificity (DPST, 75.0%; MMSE, 76.5%; MoCA, 76.2%), and positive likelihood ratio (DPST, 3.41; MMSE, 3.62; MoCA, 3.79) were similar. Conclusion: Digital Processing Speed Test, a free, automated, multilingual cognitive test conducted on a mobile device, has similar test performance to MMSE and MoCA. Nonetheless, DPST does not capture the multidomain cognitive deficits that characterize MCI/dementia. Moreover, test-retest reliability and interrater agreement of artificial intelligence-based handwriting recognition needs further confirmation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".