Predicting lung mass density of patients with interstitial lung disease and healthy subjects using deep neural network and lung ultrasound surface wave elastography.

The Hounsfield unit (HU) obtained from high resolution computed tomography (HRCT) has been used to assess lung pathology. However, lung mass density has not been quantified in vivo noninvasively. The objective of this study was to develop a method for analyzing lung mass density of superficial lung tissue of patients with interstitial lung disease (ILD) and healthy subjects using a deep neural network (DNN) and lung ultrasound surface wave elastography (LUSWE). Surface wave speeds at three vibration frequencies (100, 150 and 200 Hz) from LUSWE and a pulmonary function test (PFT) including predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) were used. Predefined lung mass densities based on the HU for ILD patients and healthy subjects (77 in total) were also used to train the DNN model. The DNN was composed of four hidden layers of 1024 neurons for each layer and trained for 80 epochs with a batch size of 20. The learning rate was 0.001. Performances of two types of activation functions in the DNN, rectified linear activation unit (ReLU) and exponential linear unit (ELU), as well as, machine learning models (support vector regression, random forest, Adaboost) were evaluated. The test dataset of wave speeds, FEV1% pre and FEV%/FVC%, was used to predict lung mass density. The results showed that predictions using a DNN with ELU obtained a comparatively better performance in the testing dataset (accuracy = 0.89) than those of DNN with ReLU or machine learning models. This method may be useful to noninvasively analyze lung mass density by using the DNN model together with the measurements from LUSWE and PFT.