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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 14, 2025; 31(34): 108807
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.108807
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.108807
Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists
Suguru Miida, Hiroteru Kamimura, Saori Endo, Hiroki Maruyama, Tomoaki Yoshida, Yusuke Watanabe, Naruhiro Kimura, Hiroyuki Abe, Akira Sakamaki, Takeshi Yokoo, Shuji Terai, Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan
Shinya Fujiki, Takayuki Inomata, Department of Cardiovascular Medicine, Niigata University Medical and Dental Hospital, Niigata 951-8510, Japan
Taichi Kobayashi, Division of Oral and Maxillofacial Radiology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
Masanori Tsukada, Fujito Numano, Department of Pediatrics, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
Yuma Fuzawa, Tetsuhiro Hirata, Yosuke Horii, Hiroyuki Ishikawa, Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
Hirofumi Nonaka, Department of Business Administration, Aichi Institute of Technology, Aichi 461-8641, Japan
Kenya Kamimura, Department of General Medicine, Niigata University School of Medicine, Niigata 951-8520, Japan
Author contributions: Miida S and Kamimura H conceptualized the study; Miida S, Kamimura H, Endo S, Maruyama H and Kobayashi T were responsible for data entry and verification; Tsukada M, Numano F, Fujiki S, Kashimura T, Inomata T, Fuzawa Y, Hirata T, Horii Y, and Ishikawa H reviewed the echocardiographic sections and performed image analysis; Kamimura H and Nonaka H conducted the data analysis; Miida S, Kamimura H, and Terai S drafted the manuscript; Yoshida T, Watanabe Y, Kimura N, Abe H, Sakamaki A, Yokoo T, and Kamimura K reviewed and approved the final version of the manuscript.
Supported by Grant-in-Aid for Research on Hepatitis from the Japan Agency for Medical Research and Development, No. 24fk0210128h0002; and Grant-in-Aid for Scientific Research, No. KAKENHI-23K07372.
Institutional review board statement: This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Niigata University (Approval Number: 2020-0199).
Informed consent statement: An opt-out option was provided on the hospital website for patients who declined participation.
Conflict-of-interest statement: The authors declare no conflict of interest related to this study.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: The datasets generated and analyzed during the current study are not publicly available due to institutional regulations, but may be made available by the corresponding author upon reasonable request at hiroteruk@med.niigata-u.ac.jp.
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/
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
Received: April 24, 2025
Revised: June 23, 2025
Accepted: August 19, 2025
Published online: September 14, 2025
Processing time: 134 Days and 20.8 Hours
Revised: June 23, 2025
Accepted: August 19, 2025
Published online: September 14, 2025
Processing time: 134 Days and 20.8 Hours
Core Tip
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, noninvasive 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.