ArticleQuantitative imaging in medicine and surgery2018
Discrimination of smoking status by MRI based on deep learning method.
Article in Quantitative imaging in medicine and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment.Quantitative imaging in medicine and surgery · 2022Article
- Dynamic models of stress-smoking responses based on high-frequency sensor data.NPJ digital medicine · 2021Article
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Study on automatic detection and classification of breast nodule using deep convolutional neural network system.Journal of thoracic disease · 2020Article
- Whole-brain morphometric studies in alcohol addicts by voxel-based morphometry.Annals of translational medicine · 2019Article
- The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images.Quantitative imaging in medicine and surgery · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status.
methodsThe 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).
resultsIn 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 (<70%) obtained with support vector machine (SVM) methods.
conclusionsThe 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.
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