Automated Evaluation of Conventional Clock-Drawing Test Using Deep Neural Network: Potential as a Mass Screening Tool to Detect Individuals With Cognitive Decline

Abstract

Introduction: The Clock-Drawing Test (CDT) is a simple cognitive tool to examine multiple domains of cognition including executive function. We aimed to build a CDT-based deep neural network (DNN) model using data from a large cohort of older adults, to automatically detect cognitive decline, and explore its potential as a mass screening tool. Methods: Over 40,000 CDT images were obtained from the National Health and Aging Trends Study (NHATS) database, which collects the annual surveys of nationally representative community-dwelling older adults in the United States. A convolutional neural network was utilized in deep learning architecture to predict the cognitive status of participants based on drawn clock images. Results: The trained DNN model achieved balanced accuracy of 90.1 ± 0.6% in identifying those with a decline in executive function compared to those without [positive likelihood ratio (PLH) = 16.3 ± 6.8, negative likelihood ratio (NLH) = 0.14 ± 0.03], and 77.2 ± 2.7 % balanced accuracy for identifying those with probable dementia from those without (PLH = 5.1 ± 0.5, NLH = 0.37 ± 0.07). Conclusions: This study demonstrated the feasibility of implementing conventional CDT to be automatically evaluated by DNN with a fair performance in a larger scale than ever, suggesting its potential as a mass screening test for ruling-in or ruling-out those with executive dysfunction or with probable dementia.