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Deep Learning Radiomics on Shear Wave Elastography and B-mode Ultrasound Videos of Diaphragm for Weaning Outcome Prediction by Changchun Li; Yan Liu; Rui Dong; Tianjie Zhang; Ye Song; Qi Zhang is a Medicine article available to read on EtoBox.

What is Deep Learning Radiomics on Shear Wave Elastography and B-mode Ultrasound Videos of Diaphragm for Weaning Outcome Prediction about?

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

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Author

Changchun Li; Yan Liu; Rui Dong; Tianjie Zhang; Ye Song; Qi Zhang

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Medicine (Health Sciences)