Early detection of dementia and mild cognitive impairment with BrainCheck
Bibliographic record
Abstract
Abstract Background Nearly 14 million people in the United States and upwards of 152 million people globally in the coming decades are suffering from dementia. Early detection of dementia provides access to timely interventions and knowledge to improve patient health and quality of life before symptoms become severe. However, current rates of undetected dementia are reported as high as 61.7%. Mild cognitive impairment (MCI) is an early symptomatic clinical stage of dementia, thus, identification of this level of cognitive impairment is important for early detection. Current clinical practices, cognitive screening assessments (MoCA, etc.), and neuropsychological testing needed for the diagnosis of MCI shows itself to be time‐consuming and resource‐limited. An accurate and reliable computerized cognitive tool that could be more rapid and maximize accessibility to both patients and providers is needed to address the expected uptick in dementia, especially in the current era of practicing amidst the COVID‐19 pandemic. BrainCheck is a computerized cognitive testing tool and has been validated previously for its diagnostic accuracy for dementia‐related cognitive decline. In this study, our objective was to evaluate BrainCheck’s ability to distinguish between Dementia, Mild Cognitive Impairment (MCI), and Normal Cognitive (NC) for the potential use of early dementia detection. Method Ninety‐nine participants associated with the University of Washington’s Memory and Brain Wellness Center were clinically evaluated with a form of Dementia (n=42), Mild Cognitive Impairment (MCI)(n=22), or had Normal Cognition (NC)(n=35). Individual BrainCheck assessments and the Braincheck Overall Scores were compared statistically among their diagnostic groups. Also, Pearson correlation coefficients were calculated between participant BrainCheck Overall Scores and their MoCA scores. Result We found significant differences between the NC, MCI and Dementia groups based on the participants’ BrainCheck battery performances, where participants with more severe cognitive impairment performed worse across the individual assessments and on BrainCheck Overall Scores. Braincheck Overall Scores also achieved >88% sensitivity/specificity for separating NC from Dementia, and >77% sensitivity/specificity in separating the MCI group from NC/Dementia groups. Furthermore, Braincheck Overall Scores highly correlated with MoCA scores (ρ=0.69, p<0.001). Conclusion BrainCheck distinguished between diagnoses of Dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".