Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features - Tan, Xin; Wu, Jinjian; Ma, Xiaomeng; Kang, Shangyu; Yue, Xiaomei; Rao, Yawen; Li, Yifan; Huang, Haoming; Chen, Yuna; Lyu, Wenjiao; Qin, Chunhong; Li, Mingrui; et al. (Frontiers in Neuroscience, 2022)
Purpose Cognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment at the early stage of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment. Methods In this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of patients. Results The classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%. Conclusions The model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment.