# Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment

## TL;DR

A new method uses digital cognitive assessments to accurately identify cognitive impairment and dementia, reducing the need for lengthy in-person evaluations.

## Contribution

A semi-supervised clustering approach is introduced to enhance digital cognitive assessments with limited labeled data.

## Key findings

- The model accurately identified cognitive groups using a combination of large and small datasets.
- Strong associations with traditional assessments validated the digital method's effectiveness.
- The approach supports scalable cognitive evaluations in non-specialist settings.

## Abstract

Traditional neuropsychological assessment for diagnosis of mild cognitive impairment (MCI) or dementia requires a lengthy in-clinic evaluation by a specialist. This creates a substantial patient burden and prolonged diagnostic and treatment timelines. Digital cognitive assessments (DCA) offer a scalable solution to meet these challenges, but their validation is challenged by the scarcity of large, high-quality datasets with established ground truth. We applied a semi-supervised model-based clustering method to combine a large dataset (N=1189) of the Digital Assessment of Cognition (DAC), a brief, remote-capable DCA, with a smaller dataset pairing DAC assessments with ground-truth neuropsychological diagnoses (N=248). The resulting model identified cognitively unimpaired, MCI, and dementia groups with high accuracy on an external test dataset. Congruent validity was established through strong expected associations with traditional analog assessments. These results validate prior exploratory work and demonstrate the potential for more nuanced, holistic, and scalable cognitive assessments in non-specialist settings.