A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease - Almubark, Ibrahim; Chang, Lin-Ching; Shattuck, Kyle F.; Nguyen, Thanh; Turner, Raymond Scott; Jiang, Xiong (Frontiers in Aging Neuroscience, 2020)
INTRODUCTION: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both. METHODS: A neuropsychological battery and a simple 5-minute cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild AD, and 41 demographically match controls. A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used. RESULTS: MLP network outperformed traditional algorithms with the all datasets. In particularly, MLP network with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. DISCUSSION: Deep learning with data from specific cognitive task(s) may have the potential to assist in the early diagnosis of Alzheimer’s disease.