Skip to content Skip to navigation

A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

Publication Type:

Journal Article


Ultrasound Med Biol, Volume 46, Number 1, p.26-33 (2020)


1879-291X (Electronic)<br/>0301-5629 (Linking)

Accession Number:




Liver fibrosis, machine learning, Shear wave elastography, Ultrasound


The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different vendors is comparable to that of magnetic resonance elastography (MRE) in distinguishing non-significant (<F2) from significant (>/=F2) fibrosis. We included two patient groups with liver disease: (i) 144 patients undergoing pSWE (Siemens) and MRE; and (ii) 60 patients undergoing 2-DSWE (Philips) and MRE. Four ML algorithms using 10 SWV measurements as inputs were trained with MRE. Results were validated using twofold cross-validation. The performance of median SWV in binary grading of fibrosis was moderate for pSWE (area under the curve [AUC]: 0.76) and 2-DSWE (0.84); the ML algorithm support vector machine (SVM) performed particularly well (pSWE: 0.96, 2-DSWE: 0.99). The results suggest that the multivendor ML-based algorithm SVM can binarily grade liver fibrosis using ultrasound elastography with excellent diagnostic performance, comparable to that of MRE.