@article{2985626,
    title = "A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography",
    author = "Gatos, I. and Tsantis, S. and Spiliopoulos, S. and Karnabatidis, D. and Theotokas, I. and Zoumpoulis, P. and Loupas, T. and Hazle, J.D. and Kagadis, G.C.",
    journal = "Ultrasound in Medicine and Biology",
    year = "2017",
    volume = "43",
    number = "9",
    pages = "1797-1810",
    publisher = "ELSEVIER SCIENCE INC 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA",
    issn = "0301-5629",
    doi = "10.1016/j.ultrasmedbio.2017.05.002",
    keywords = "Artificial intelligence;  Clustering algorithms;  Color;  Computer aided diagnosis;  Computer aided instruction;  Diagnosis;  Learning systems;  Medical imaging;  Regression analysis;  Shear flow;  Shear waves;  Shearing machines;  Stiffness;  Support vector machines;  Ultrasonic applications;  Ultrasonics, Classifier design;  Computer aided diagnosis systems;  Fibrosis;  Receiver operating characteristic curve analysis;  Shear wave elastography;  Stepwise regression analysis;  Support vector machine classification;  Support vector machine models, Learning algorithms, adult;  aged;  Article;  chronic liver disease;  classification algorithm;  computer assisted diagnosis;  controlled study;  diagnostic accuracy;  female;  human;  liver stiffness;  machine learning;  major clinical study;  male;  predictive value;  priority journal;  radial based function;  real time ultrasound scanner;  receiver operating characteristic;  sensitivity and specificity;  shear wave elastography;  support vector machine;  adolescent;  algorithm;  chronic disease;  color;  diagnostic imaging;  elastography;  liver;  liver disease;  middle aged;  procedures;  young adult, Adolescent;  Aged;  Algorithms;  Chronic Disease;  Color;  Diagnosis, Computer-Assisted;  Elasticity Imaging Techniques;  Female;  Humans;  Liver;  Liver Diseases;  Machine Learning;  Male;  Middle Aged;  Sensitivity and Specificity;  Young Adult",
    abstract = "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. © 2017 World Federation for Ultrasound in Medicine & Biology"
}