TY - JOUR AU - Wang, Shuangkun AU - Zhang, Rongguo AU - Deng, Yufeng AU - Chen, Kuan AU - Xiao, Dan AU - Peng, Peng AU - Jiang, Tao PY - 2018 TI - Discrimination of smoking status by MRI based on deep learning method JF - Quantitative Imaging in Medicine and Surgery; Vol 8, No 11 (December 29, 2018): Quantitative Imaging in Medicine and Surgery Y2 - 2018 KW - N2 - Background: This study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status. Methods: The head MRI 3D-T1WI images of 127 subjects (61 smokers and 66 non-smokers) were collected, and 176 image slices obtained for each subject. These subjects were 23–45 years old, and the smokers had at least 5 years of smoking experience. Approximate 25% of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the remaining subjects as the training set. Two deep learning models were developed: deep 3D convolutional neural network (Conv3D) and convolution neural network plus a recurrent neural network (RNN) with long short-term memory architecture (ConvLSTM). Results: In the prediction of smoking status, Conv3D model achieved an accuracy of 80.6% (25/31), a sensitivity of 80.0% and a specificity of 81.3%, and ConvLSTM model achieved an accuracy of 93.5% (29/31), a sensitivity of 93.33% and a specificity of 93.75%. The accuracy obtained by these methods was significantly higher than that ( Conclusions: The deep learning-based MRI can accurately predict smoking status. Studies with large sample size are needed to improve the accuracy and to predict the level of nicotine dependence. UR - https://qims.amegroups.org/article/view/22979