Estimating age-related changes in in vivo cerebral magnetic resonance angiography using convolutional neural network.

Although age-related changes of cerebral arteries were observed in in vivo magnetic resonance angiography (MRA), standard tools or methods measuring those changes were limited. In this study, we developed and evaluated a model to measure age-related changes in the cerebral arteries from 3D MRA using a 3D deep convolutional neural network. From participants without any medical abnormality, training (n = 800) and validation sets (n = 88) of 3D MRA were built. After preprocessing and data augmentation, a 3D convolutional neural network was trained to estimate each subject's chronological age from in vivo MRA data. There was good correlation between chronological age and predicted age (r = 0.83) in an independent test set (n = 354). The predicted age difference (PAD) of the test set was 2.41 ± 6.22. Interaction term between age and sex was significant for PAD (p = 0.008). After correcting for age and interaction term, men showed higher PAD (p < 0.001). Hypertension was associated with higher PAD with marginal significance (p = 0.073). We suggested that PAD might be a potential measurement of cerebral vascular aging. • There was good correlation between age at the time of MRI and predicted age from brain MRA (r = 0.83). • Using deep learning, we can assess the appropriateness of the overall cerebral vascular aging status of each subject. • This system can be used to assess cerebral vascular aging in subjects with atherosclerosis risk factors, such as hypertension. • A deep learning machine showed potential for assessing age-related changes with brain MRA without knowledge-based features. [ABSTRACT FROM AUTHOR]