Abstract
Developing tools for efficiently measuring cognitive change specifically and brain health generally—whether for clinical use or as endpoints in clinical trials—is a major challenge, particularly for conditions such as Alzheimer's disease. Technology such as connected devices and advances in artificial intelligence offer the possibility of creating and deploying clinical-grade tools with high sensitivity, rapidly, cheaply, and non-intrusively. Starting from a widely-used paper and pencil cognitive status test—The Clock Drawing Test—we combined a digital input device to capture time-stamped drawing coordinates with a machine learning analysis of drawing behavior to create DCTclock™, an automated analysis of nuances in cognitive performance beyond successful task completion. Development and validation was conducted on a dataset of 1,833 presumed cognitively unimpaired and clinically diagnosed cognitively impaired individuals with varied neurological conditions. We benchmarked DCTclock against existing clock scoring systems and the Mini-Mental Status Examination, a widely-used but lengthier cognitive test, and showed that DCTclock offered a significant improvement in the detection of early cognitive impairment and the ability to characterize individuals along the Alzheimer's disease trajectory. This offers an example of a robust framework for creating digital biomarkers that can be used clinically and in research for assessing neurological function.
Highlights
Detection and diagnosis of cognitive decline is critical to the development and deployment of novel therapeutic interventions for patients with dementia due to Alzheimer’s disease (AD) and other neurodegenerative diseases
We describe how the algorithms were developed, the resulting clinical scores, and the validation findings based on comparisons between AD and amnestic mild cognitive impairment (aMCI) clinical samples and a community-based normal aging population
DCTclock Score does not show a significant correlation with the Beck Depression Inventory and the Geriatric Depression Scale; the low correlations indicate that DCTclock is not measuring indices associated with depression or emotional state, but rather are specific to cognition
Summary
Detection and diagnosis of cognitive decline is critical to the development and deployment of novel therapeutic interventions for patients with dementia due to Alzheimer’s disease (AD) and other neurodegenerative diseases. DCTclock: AI Analysis of Drawing Behavior cognitive screening tasks such as the Mini Mental State Examination (MMSE) [1] or the Montreal Cognitive Assessment (MoCA) [2] These tests have been shown to be, relatively insensitive to milder forms of impairment and require administration and hand-scoring by trained administrators, which can lead to subjectivity in both scoring and interpretation. Analyzing the entire drawing process offers better opportunity to capture early change by revealing subtle behaviors that precede impairment such as decision-making latencies, compensatory strategies, and psycho-motor issues not visible in the final drawing To address these issues, a first digital version of the CDT was created by using a digitizing pen to capture the entire drawing process—both spatial and temporal data—while keeping the well-known administration procedures for the clock drawing test administration standard. DCTclock is FDA-cleared to market as a computerized cognitive assessment aid, offering a sensitive and scalable early detection method for cognitive health and dementia
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