| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Observational Study |
| Article Title |
Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Bowen Li, Dar-In Tai, Ke Yan, Yi-Cheng Chen, Cheng-Jen Chen, Shiu-Feng Huang, Tse-Hwa Hsu, Wan-Ting Yu, Jing Xiao, Lu Le and Adam P Harrison |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Maintenance Project of the Center for Artificial Intelligence |
No. CLRPG3H0012 and No. SMRPG3I0011 |
|
| Corresponding Author |
Dar-In Tai, MD, PhD, Professor, Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fuxing Street, Guishan Dist, Taoyuan 33305, Taiwan. tai48978@cgmh.org.tw |
| Key Words |
Ultrasound; Liver steatosis; Deep learning; Screening; Computer-aided diagnosis |
| Core Tip |
Ultrasound is widely used to evaluate liver steatosis, but it is subjective. We developed a deep learning algorithm for quantitative steatosis scoring from ultrasound. The algorithm was trained on > 200000 images and composed of different scanners and viewpoints from both hepatic lobes. High diagnostic performance was measured across all viewpoints in separate histology proven groups, which was comparable to or better than the control attenuation parameter. We demonstrated high agreement across scanners and viewpoints. Thus, our deep learning algorithm provides a quantitative assessment with high performance and reliability. |
| Publish Date |
2022-06-10 05:40 |
| Citation |
Li B, Tai DI, Yan K, Chen YC, Chen CJ, Huang SF, Hsu TH, Yu WT, Xiao J, Le L, Harrison AP. Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning. World J Gastroenterol 2022; 28(22): 2494-2508 |
| URL |
https://www.wjgnet.com/1007-9327/full/v28/i22/2494.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v28.i22.2494 |
