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Discrimination of smoking status by MRI based on deep learning method
Shuangkun Wang1, Rongguo Zhang2, Yufeng Deng2
1Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 10020, China.
Quantitative Imaging in Medicine and Surgery
|February 1, 2019
Summary
Deep learning models utilizing magnetic resonance imaging (MRI) accurately predict smoking status. These advanced techniques show higher accuracy than traditional methods, paving the way for future research into nicotine dependence.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background:
- Assessing smoking status is crucial for public health.
- Current methods for determining smoking status can be subjective.
- Objective biomarkers for smoking status are needed.
Purpose of the Study:
- To evaluate the feasibility of deep learning (DL) models using MRI for predicting smoking status.
- To compare the performance of different DL architectures in this task.
Main Methods:
- Collected head MRI 3D-T1WI images from 127 participants (61 smokers, 66 non-smokers).
- Developed and tested two DL models: a 3D convolutional neural network (Conv3D) and a convolutional neural network with a recurrent neural network (ConvLSTM).
- Utilized a training and testing split, with approximately 25% of subjects in the test set.
Main Results:
- The Conv3D model achieved 80.6% accuracy, while the ConvLSTM model reached 93.5% accuracy in predicting smoking status.
- The ConvLSTM model demonstrated superior performance with 93.33% sensitivity and 93.75% specificity.
- Both DL models significantly outperformed traditional Support Vector Machine (SVM) methods, which had <70% accuracy.
Conclusions:
- Deep learning-based MRI analysis is a feasible and accurate method for predicting smoking status.
- Further research with larger sample sizes is recommended to enhance prediction accuracy and explore nicotine dependence levels.
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