Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks.

ABSTRACT:

Objectives

To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney).

Methods

US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quant

SUBMITTER: Byra M 

PROVIDER: S-EPMC9838564 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature