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

PurposeCognitive 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. MethodsIn 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. ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%. ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment.

Automated summary

This study utilized a convolutional neural network to construct a model for classifying T2DM patients into cognitive impairment and non-cognitive impairment groups based on T1-weighted structural MRI. The results showed that the model could accurately identify T2DM-related cognitive decline, providing clinicians with a tool for analyzing and predicting cognitive impairment in patients.