Brief Digital Cognitive Assessment Identifies MCI and Dementia via Semi-Supervised Clustering (Preprint)
Year: 2026
Type: preprint
Abstract: BACKGROUND Traditional neuropsychological assessments for cognitive decline are lengthy in-clinic evaluations by a specialist, with typical wait times of 6-8 months. This creates a substantial patient burden and prolonged diagnostic and treatment timelines. Digital cognitive assessments (DCA) offer a scalable solution to these challenges, but their validation is challenged by the scarcity of large, high-quality datasets with established ground truth. OBJECTIVE To develop a model to identify mild cognitive impairment (MCI) and probable dementia using metrics from the Digital Assessment of Cognition (DAC), a brief, remote-capable DCA. A secondary objective was to conduct a preliminary assessment of the model's validity. METHODS We applied a semi-supervised model-based clustering method to combine a large dataset (N=1189) of DAC assessments alone, with a smaller dataset pairing DAC assessments with ground-truth neuropsychological diagnoses (N=248). We examined the model's predictive validity by comparing its predictions with diagnoses on a held-out test set. We examined congruent validity by testing associations with traditional analog assessments and demographic variables. RESULTS We identified a 6-cluster model with 3 MCI clusters and 2 probable dementia clusters. The model identified cognitively unimpaired, MCI, and dementia groups with high accuracy (78.7%) on the held-out test dataset, and showed excellent ability to identify cognitive impairment (AUROC=0.985) and dementia (AUROC=0.932). We identified strong associations with traditional analog assessments and demographic variables. An exploratory analysis showed evidence that clusters correspond to clinically meaningful subtypes of MCI. CONCLUSIONS These results validate prior exploratory work and demonstrate the potential for more nuanced, holistic, and scalable cognitive assessments in non-specialist settings.
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7162947045