Diagnosis of Early Mild Cognitive Impairment in Type 2 Diabetes Mellitus by Deep Learning of Multimodal Neuroimages and Metadata

Motivation: Identification of early cognitive impairment in type 2 diabetes mellitus (T2DM) patients is of paramount importance for mitigating cognitive decline of patients and enhancing their quality of life. Goal(s): Our objective was to develop a robust deep learning model for diagnosing early cognitive impairment in T2DM using multi-modal neuroimages. Approach: We developed a multi-modal neural network, which incorporated informative clinical metadata (i.e., MoCA, BMI and HbA1c) to design metadata-induced contrastive Laplacian regularization. Results: The proposed approach demonstrated significant improvement in accuracy in the identification of T2DM with/without mild cognitive impairment in a dataset with 311 subjects. Impact: Superior diagnostic performance of the proposed method for early cognitive impairment in T2DM demonstrates its ability in understanding of T2DM cognitive impairment associated brain alterations and its potential applications on other brain disorders.