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...
| Publicado en: | Ultrasound in Medicine & Biology Vol. 43; no. 9; pp. 1797 - 1811 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124142770&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124142770 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03015629 JJ6 jtl: Ultrasound in Medicine & Biology issn: 03015629 maglogo: N pubinfo: dt: Sep2017 vid: 43 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 124142770 124142770 NLM28634041 124142770 10.1016/j.ultrasmedbio.2017.05.002 NLM28634041 124142770 ppf: 1797 ppct: 14 formats: tig: atl: A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography. aug: au: Gatos, Ilias Tsantis, Stavros Spiliopoulos, Stavros Karnabatidis, Dimitris Theotokas, Ioannis Zoumpoulis, Pavlos Loupas, Thanasis Hazle, John D. Kagadis, George C. affil: Department of Medical Physics, School of Medicine, University of Patras, Rion, Greece sug: subj: Diagnosis, Computer Assisted Methods Ultrasonography Methods Liver Diseases Aged Female Sensitivity and Specificity Adolescence Color Male Young Adult Chronic Disease Liver Middle Age Algorithms Human Aged: 65+ years Adolescent: 13-18 years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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