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

Guilherme Moura Cunha¹, Kyle A Hasenstab², Atsushi Higaki¹

¹Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, United States.

European Journal of Radiology

|January 21, 2020

Summary

A new artificial intelligence algorithm accurately assesses the quality of liver MRI scans, potentially reducing scan times. This convolutional neural network (CNN) tool helps radiologists identify suboptimal hepatobiliary phase (HBP) images.

Keywords:

Gd-EOB-DTPA Liver Magnetic resonance imaging

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Published on: October 10, 2018

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Machine Learning for Healthcare

Background:

Gadoxetate disodium (EOB)-enhanced MRI is crucial for liver imaging.
Assessing the hepatobiliary phase (HBP) adequacy is vital for accurate diagnosis.
Manual assessment of HBP adequacy can be time-consuming and subjective.

Purpose of the Study:

To develop and evaluate an automated convolutional neural network (CNN) algorithm for assessing EOB-enhanced MRI HBP adequacy.
To explore the potential of the CNN algorithm in reducing MRI examination length.

Main Methods:

Retrospective analysis of 1408 EOB-enhanced MRI-HBP series from 2011-2018.
Development of a CNN comprising liver segmentation and classification for adequacy scoring.
Comparison of CNN performance against consensus radiologist assessment using ROC analysis and AUC.

Main Results:

The CNN algorithm achieved high performance with Area Under the Curve (AUC) values of 0.97 (internal) and 0.95 (external).
Reader agreement (Cohen's kappa) ranged from 0.67 to 0.80.
The algorithm indicated that 48% of examinations could have been shortened.

Conclusions:

The CNN-based algorithm demonstrates high accuracy (>95% AUC) in classifying HBP image adequacy.
This AI tool can assist radiologists in identifying suboptimal images, potentially reducing diagnostic errors.
Application of the algorithm may lead to shorter MRI examination times.