Risk Assessment of Alzheimer's Disease using the Information Diffusion Model from Structural Magnetic Resonance Imaging

Abstract

Background: Recently, automatic risk assessment methods have been a target for the detection of Alzheimer's disease (AD) risk. Objective: This study aims to develop an automatic computer-aided AD diagnosis technique for risk assessment of AD using information diffusion theory. Methods: Information diffusion is a fuzzy mathematics logic of set-value that is used for risk assessment of natural phenomena, which attaches fuzziness (uncertainty) and incompleteness. Data were obtained from voxel-based morphometry analysis of structural magnetic resonance imaging. Results and Conclusion: The information diffusion model results revealed that the risk of AD increases with a reduction of the normalized gray matter ratio (p > 0.5, normalized gray matter ratio <40%). The information diffusion model results were evaluated by calculation of the correlation of two traditional risk assessments of AD, the Mini-Mental State Examination and the Clinical Dementia Rating. The correlation results revealed that the information diffusion model findings were in line with Mini-Mental State Examination and Clinical Dementia Rating results. Application of information diffusion model contributes to the computerization of risk assessment of AD, which has a practical implication for the early detection of AD.