Open AccessDeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults
Author(s) -
Hailong Li,
Lili He,
Jonathan Dudley,
Thomas Maloney,
Elanchezhian Somasundaram,
Samuel L. Brady,
Nehal A. Parikh,
Jonathan R. Dillman
Publication year - 2020
Publication title -
pediatric radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 86
eISSN - 1432-1998
pISSN - 0301-0449
DOI - 10.1007/s00247-020-04854-3
Subject(s) - medicine , magnetic resonance elastography , receiver operating characteristic , magnetic resonance imaging , neuroradiology , elastography , chronic liver disease , radiology , ultrasound , cirrhosis , neurology , psychiatry
Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs.