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A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.
- Source :
-
Ultrasound in Medicine & Biology . Jan2020, Vol. 46 Issue 1, p26-33. 8p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03015629
- Volume :
- 46
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Ultrasound in Medicine & Biology
- Publication Type :
- Academic Journal
- Accession number :
- 141779190
- Full Text :
- https://doi.org/10.1016/j.ultrasmedbio.2019.09.004