Extended Application of Digital Clock Drawing Test in the Evaluation of Alzheimer's Disease Based on Artificial Intelligence and the Neural Basis

Abstract

Introduction: This study aimed to build the supervised learning model to predict the state of cognitive impairment, Alzheimer's Disease (AD) and cognitive domains including memory, language, action, and visuospatial based on Digital Clock Drawing Test (dCDT) precisely.

Methods: 207 normal controls, 242 Mild Cognitive Impairment (MCI) patients, 87 dementia patients, including 53 AD patients, were selected from Shanghai Tongji Hospital. The electromagnetic tablets were used to collect the trajectory points of dCDT. By combining dynamic process and static results, different types of features were extracted, and the prediction models were built based on the feature selection approaches and machine learning methods.

Results and discussion: The optimal AUC of cognitive impairment's screening, AD's screening and differentiation are 0.782, 0.919 and 0.818, respectively. In addition, the cognitive state of the domains with the best prediction result based on the features of dCDT is action with the optimal AUC 0.794, while the other three cognitive domains got the prediction results between 0.744-0.755.

Conclusion: By extracting dCDT features, cognitive impairment and AD patients can be identified early. Through dCDT feature extraction, a prediction model of single cognitive domain damage can be established.

Keywords: Alzheimer’s disease; brain atrophy; cognitive domains; dementia.; digital clock drawing test; machine learning.