| 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) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Observational Study |
| Article Title |
Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Suguru Miida, Hiroteru Kamimura, Shinya Fujiki, Taichi Kobayashi, Saori Endo, Hiroki Maruyama, Tomoaki Yoshida, Yusuke Watanabe, Naruhiro Kimura, Hiroyuki Abe, Akira Sakamaki, Takeshi Yokoo, Masanori Tsukada, Fujito Numano, Takeshi Kashimiura, Takayuki Inomata, Yuma Fuzawa, Tetsuhiro Hirata, Yosuke Horii, Hiroyuki Ishikawa, Hirofumi Nonaka, Kenya Kamimura and Shuji Terai |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Grant-in-Aid for Research on Hepatitis from the Japan Agency for Medical Research and Development |
24fk0210128h0002 |
| Grant-in-Aid for Scientific Research |
KAKENHI-23K07372 |
|
| Corresponding Author |
Hiroteru Kamimura, MD, PhD, Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757 Asahimachi Dori, Niigata 951-8520, Japan. hiroteruk@med.niigata-u.ac.jp |
| Key Words |
Machine learning; Liver congestion; Heart failure; Artificial intelligence; Image analysis |
| Core Tip |
Using ResNet-based deep learning on paraumbilical vein-level computed tomography images from 179 patients with chronic heart failure, we developed a model to predict tricuspid regurgitation severity. The model outperformed six gastroenterologists and three radiologists, excelling particularly in identifying severe TR. This novel, non invasive approach captures subtle hepatic congestion features, enabling earlier detection of liver dysfunction secondary to heart failure. Our findings highlight the potential of machine learning to enhance diagnostic accuracy and guide timely intervention in congestive hepatopathy management. |
| Publish Date |
2025-09-05 06:36 |
| Citation |
<p>Miida S, Kamimura H, Fujiki S, Kobayashi T, Endo S, Maruyama H, Yoshida T, Watanabe Y, Kimura N, Abe H, Sakamaki A, Yokoo T, Tsukada M, Numano F, Kashimiura T, Inomata T, Fuzawa Y, Hirata T, Horii Y, Ishikawa H, Nonaka H, Kamimura K, Terai S. Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists. <i>World J Gastroenterol</i> 2025; 31(34): 108807</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i34/108807.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i34.108807 |
