Souillard-Mandar, William and Penney, Dana and Schaible, Braydon and Pascual-Leone, Alvaro and Au, Rhoda and Davis, Randall (2021) DCTclock: Clinically-Interpretable and Automated Artificial Intelligence Analysis of Drawing Behavior for Capturing Cognition. Frontiers in Digital Health, 3. ISSN 2673-253X
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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.
Item Type: | Article |
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Subjects: | Scholar Eprints > Multidisciplinary |
Depositing User: | Managing Editor |
Date Deposited: | 26 Nov 2022 04:35 |
Last Modified: | 05 Sep 2024 11:55 |
URI: | http://repository.stmscientificarchives.com/id/eprint/373 |