Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

Background: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests.

Aging in the Digital Age: Using Technology to Increase the Reach of the Clinician Expert and Close the Gap Between Health Span and Life Span.

Age-related cognitive impairment (ARCI) has a profound impact on individuals, families, health care systems, and societies at large. Evidence suggests that ARCI is the consequence of underlying brain pathology. Therefore, efforts to minimize the impact of ARCI and thus closing the gap between health span and life span, which has widened in recent years, requires early detection and timely deployment of targeted, personalized interventions.

DCTclock: Clinically-Interpretable and Automated Artificial Intelligence Analysis of Drawing Behavior for Capturing Cognition.

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.

Association of Digital Clock Drawing With PET Amyloid and Tau Pathology in Normal Older Adults.

Objective: To determine whether a digital clock-drawing test, DCTclock, improves upon standard cognitive assessments for discriminating diagnostic groups and for detecting biomarker evidence of amyloid and tau pathology in clinically normal older adults (CN).

Methods: Participants from the Harvard Aging Brain Study and the PET laboratory at Massachusetts General Hospital were recruited to undergo the DCTclock, standard neuropsychological assessments including the Preclinical Alzheimer Cognitive Composite (PACC), and amyloid/tau PET imaging. Receiver operating curve analyses were used to assess diagnostic and biomarker discriminability.

Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test.

The Clock Drawing Test - a simple pencil and paper test - has been used for more than 50 years as a screening tool to differentiate normal individuals from those with cognitive impairment, and has proven useful in helping to diagnose cognitive dysfunction associated with neurological disorders such as Alzheimer's disease, Parkinson's disease, and other dementias and conditions. We have been administering the test using a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making available far more detailed data about the subject's performance. Using pen stroke data from these drawings categorized by our software, we designed and computed a large collection of features, then explored the tradeoffs in performance and interpretability in classifiers built using a number of different subsets of these features and a variety of different machine learning techniques.