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Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597079/ https://www.ncbi.nlm.nih.gov/pubmed/33286775 http://dx.doi.org/10.3390/e22091006 |
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author | Chen, Jheng-Ru Chao, Yi-Ping Tsai, Yu-Wei Chan, Hsien-Jung Wan, Yung-Liang Tai, Dar-In Tsui, Po-Hsiang |
author_facet | Chen, Jheng-Ru Chao, Yi-Ping Tsai, Yu-Wei Chan, Hsien-Jung Wan, Yung-Liang Tai, Dar-In Tsui, Po-Hsiang |
author_sort | Chen, Jheng-Ru |
collection | PubMed |
description | Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis. |
format | Online Article Text |
id | pubmed-7597079 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75970792020-11-09 Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis Chen, Jheng-Ru Chao, Yi-Ping Tsai, Yu-Wei Chan, Hsien-Jung Wan, Yung-Liang Tai, Dar-In Tsui, Po-Hsiang Entropy (Basel) Article Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis. MDPI 2020-09-09 /pmc/articles/PMC7597079/ /pubmed/33286775 http://dx.doi.org/10.3390/e22091006 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Jheng-Ru Chao, Yi-Ping Tsai, Yu-Wei Chan, Hsien-Jung Wan, Yung-Liang Tai, Dar-In Tsui, Po-Hsiang Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title | Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title_full | Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title_fullStr | Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title_full_unstemmed | Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title_short | Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis |
title_sort | clinical value of information entropy compared with deep learning for ultrasound grading of hepatic steatosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597079/ https://www.ncbi.nlm.nih.gov/pubmed/33286775 http://dx.doi.org/10.3390/e22091006 |
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