Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jheng-Ru, Chao, Yi-Ping, Tsai, Yu-Wei, Chan, Hsien-Jung, Wan, Yung-Liang, Tai, Dar-In, Tsui, Po-Hsiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783602255455322112
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
work_keys_str_mv AT chenjhengru clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT chaoyiping clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT tsaiyuwei clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT chanhsienjung clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT wanyungliang clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT taidarin clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis
AT tsuipohsiang clinicalvalueofinformationentropycomparedwithdeeplearningforultrasoundgradingofhepaticsteatosis