Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features.

Purpose: Cognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages 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 the 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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FL declared a past co-authorship with the author SQ to the handling editor.
(Copyright © 2022 Tan, Wu, Ma, Kang, Yue, Rao, Li, Huang, Chen, Lyu, Qin, Li, Feng, Liang and Qiu.)