Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) algorithm. The CNN model was trained by 1,530 scans in our institution. The performance was evaluated by the mean absolute error (MAE) between the predicted brain age and the chronological age based on an internal test set (n=270) and an external test set (n=560). The ensemble CNN model showed an MAE of 4.22 years in the internal test set and 9.96 years in the external test set. Participants with grade 2-3 white matter hyperintensity (WMH) showed a higher corrected predicted age difference (PAD) than grade 0 WMH (posthoc p<0.001). Participants diagnosed with diabetes mellitus also had a higher corrected PAD than those without diabetes (adjusted p=0.048), although it showed no significant differences according to the diagnosis of hypertension or dyslipidemia. We suggest that routine clinical T2-WIs are feasible to predict brain age, and it might be clinically relevant according to the WMH grade and the presence of diabetes mellitus.
Keywords: Brain age prediction; Brain aging; Brain magnetic resonance imaging; Deep convolutional neural network.
Copyright © 2021. Published by Elsevier Inc.