A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy contr...

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Vydané v:Ultrasound in medicine & biology Ročník 43; číslo 9; s. 1797
Hlavní autori: Gatos, Ilias, Tsantis, Stavros, Spiliopoulos, Stavros, Karnabatidis, Dimitris, Theotokas, Ioannis, Zoumpoulis, Pavlos, Loupas, Thanasis, Hazle, John D, Kagadis, George C
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 01.09.2017
Predmet:
Adolescent
Aged
Algorithms
Chronic Disease
Color
Diagnosis, Computer-Assisted - methods
Elasticity Imaging Techniques - methods
Female
Humans
Liver - diagnostic imaging
Liver Diseases - diagnostic imaging
Machine Learning
Male
Middle Aged
Sensitivity and Specificity
Young Adult
Shear wave elastography
Classifier design
Computer-aided diagnosis
Fibrosis
Ultrasonics
ISSN:1879-291X, 1879-291X
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Popis
Shrnutí:The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1879-291X
1879-291X
DOI:10.1016/j.ultrasmedbio.2017.05.002