A novel fusion method of 3D MRI and test results through deep learning for the early detection of Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is a prevalent form of dementia that impacts brain cells. Although its likelihood increases with age, there is no transitional period between its stages. In order to enhance diagnostic precision, physicians rely on clinical judgments derived from interpreting health data, considering demographics, clinical history, and laboratory results to detect AD at an early stage. While patient cognitive tests and demographic information are primarily presented in text, brain scan images are presented in graphic formats. Researchers typically use different classifiers for each data format and then merge the classifier outcomes to maximize classification accuracy and utilize all patient-related data for the final decision. However, this approach leads to low performance, diminishing predictive abilities and model effectiveness. We propose an innovative approach that combines diverse textual health records (HR) with three-dimensional structural magnetic resonance imaging (3D sMRI) to achieve a similar objective in computer-aided diagnosis, utilizing a novel deep learning technique. Health records, encompassing demographic features like age, gender, apolipoprotein gene, and mini-mental state examination score, are fused with 3D sMRI, enabling a graphic-based deep learning strategy for early AD detection. The fusion of data is accomplished by representing textual information as graphic pipes and integrating them into 3D sMRI, a method referred to as the “pipe-laying” method. Experimental results from over 4000 sMRI scans of 780 patients in the AD Neuroimaging Initiative (ADNI) dataset demonstrate that the pipe-laying method enhances recognition accuracy rates for Early and Late Mild Cognitive Impairment (MCI) patients, accurately classifying all AD patients. In a 4-class AD diagnosis scenario, accuracy improved from 86.87% when only 3D images were used to 90.00% when 3D sMRI and patient health records were included. Thus, the positive impact of combining 3D sMRI with HR on 4-class AD diagnosis was established.