Deep learning radiomics on shear wave elastography and b-mode ultrasound videos of diaphragm for weaning outcome prediction

Abstract

Purpose: We proposed an automatic method based on deep learning radiomics (DLR) on shear wave elastography (SWE) and B-mode ultrasound videos of diaphragm for two classification tasks, one for differentiation between the control and patient groups, and the other for weaning outcome prediction.

Materials and methods: We included a total of 581 SWE and B-mode ultrasound videos, of which 466 were from the control group of 179 normal subjects, and 115 were from the patient group of 35 mechanically ventilated subjects in the intensive care unit (ICU). Among the patient group, 17 subjects successfully weaned and 18 failed. The deep neural network of U-Net was utilized to automatically segment diaphragm regions in dual-modal videos of SWE and B-mode. High-throughput radiomics features were then extracted, the statistical test and least absolute shrinkage and selection operator (LASSO) were applied for feature dimension reduction. The optimal classification models for the two tasks were established using the support vector machine (SVM).

Results: The automatic segmentation model achieved Dice score of 87.89 %. A total of 4524 radiomics features were extracted, 10 and 20 important features were left after feature dimension reduction for constructing the two classification models. The best areas under receiver operating characteristic curves of the two models reached 84.01 % and 94.37 %, respectively.

Conclusions: Our proposed DLR methods are innovative for automatic segmentation of diaphragm regions in SWE and B-mode videos and deep mining of high-throughput radiomics features from dual-modal images. The approaches have been proved to be effective for prediction of weaning outcomes.

Keywords: Deep learning radiomics; Diaphragm image segmentation; High-throughput radiomics features; Ultrasound videos; Weaning outcome prediction.