Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists

doi:10.3748/wjg.v31.i34.108807

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World Journal of Gastroenterology

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1007-9327

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World J Gastroenterol. Sep 14, 2025; 31(34): 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, 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 Kashimura, Takayuki Inomata, Yuma Fuzawa, Tetsuhiro Hirata, Yosuke Horii, Hiroyuki Ishikawa, Hirofumi Nonaka, Kenya Kamimura, Shuji Terai

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.

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

Abstract

BACKGROUND

Congestive hepatopathy, also known as nutmeg liver, is liver damage secondary to chronic heart failure (HF). Its morphological characteristics in terms of medical imaging are not defined and remain unclear.

AIM

To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography (CT) scans.

METHODS

We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year. Right HF severity was classified into three grades. Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation (TR) severity. Model accuracy was compared with that of six gastroenterology and four radiology experts.

RESULTS

In the included patients, 120 were male (mean age: 73.1 ± 14.4 years). The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts. The model was found to be exceptionally reliable for predicting severe TR.

CONCLUSION

Deep learning models, particularly those using ResNet architectures, can help identify morphological changes associated with TR severity, aiding in early liver dysfunction detection in patients with HF, thereby improving outcomes.

Keywords: 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, 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.