DCTclock: Clinically-Interpretable and Automated Artificial Intelligence Analysis of Drawing Behavior for Capturing Cognition - Souillard-Mandar, William; Penney, Dana; Schaible, Braydon; Pascual-Leone, Alvaro; Au, Rhoda; Davis, Randall (Frontiers in Digital Health, 2021)
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 DCTclockTM, an automated analysis of nuances in cognitive performance beyond successful task completion. Development and validation was conducted on a dataset of 1833 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.