1. Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network
- Author
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Eung Koo Yeon, Tae Jin Yun, Ji Hoon Kim, Inpyeong Hwang, Roh-Eul Yoo, Seung Hong Choi, Chul-Ho Sohn, Hyeon-Jin Kim, Ji Ye Lee, and Koung Mi Kang
- Subjects
Adult ,Male ,0301 basic medicine ,Aging ,medicine.medical_specialty ,Age prediction ,Neuroimaging ,Convolutional neural network ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Predictive Value of Tests ,Diabetes Mellitus ,Humans ,Medicine ,Clinical significance ,Brain magnetic resonance imaging ,Aged ,Age differences ,business.industry ,General Neuroscience ,Brain ,Middle Aged ,White Matter ,Diffusion Magnetic Resonance Imaging ,030104 developmental biology ,Test set ,Spin echo ,Feasibility Studies ,Female ,Neural Networks, Computer ,Neurology (clinical) ,Radiology ,Geriatrics and Gerontology ,business ,T2 weighted ,030217 neurology & neurosurgery ,Developmental Biology - Abstract
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
- Published
- 2021