Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

ABSTRACT:

Purpose

To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential of the proposed CNN algorithm to reduce examination length by applying it to EOB-MRI examinations.

Methods

We retrospectively identified EOB-enhanced MRI-HBP series from examinations performed 2011-2018 (internal and external datasets). Our algorithm, comprising a liver segmentation and classification CNN, produces an adequacy score. Two abdominal radiologists independently classified series as adequate or suboptimal. The consensus determination of HBP adequacy was used as ground truth for CNN model training and validation. Reader

SUBMITTER: Cunha GM 

PROVIDER: S-EPMC7309446 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature