Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2494
Peer-review started: December 18, 2021
First decision: January 23, 2022
Revised: February 3, 2022
Accepted: April 22, 2022
Article in press: April 22, 2022
Published online: June 14, 2022
Processing time: 174 Days and 2.5 Hours
Two-dimensional (2D) ultrasound has been used for screening of liver steatosis for more than 5 decades. It is a cheap and non-invasive study.
Two-dimensional ultrasound is a subjective diagnosis that is not suitable for quantitative study.
To produce an objective steatosis diagnostic algorithm by deep learning from big data of 2D ultrasound images.
Using multi-view ultrasound big data from a retrospective cohort of patients, we trained a deep learning algorithm to diagnose steatosis stages from clinical ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded histology-proven cohorts with histopathology diagnoses and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic analysis.
The deep learning algorithm demonstrated repeatable measurements with a moderate number of images and high agreement across three premium ultrasound scanners. High diagnostic performance was observed across all viewpoints. Areas under the curve of the receiver operating characteristic to classify mild, moderate, and severe steatosis grades were 0.85, 0.91, and 0.93, respectively. This algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter on the unblinded or blinded histology-proven cohort.
This algorithm could give an objective diagnosis of steatosis from prospectively or retrospectively collected 2D ultrasound images.
The cutoff values for different grades of steatosis would need future studies in different scanners and fibrosis status.