Authors
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
Published in
World journal of gastroenterology. Volume 31. Issue 34. Pages 108807. Sep 14, 2025.
Abstract
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.
To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography (CT) scans.
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.
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.
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.
PMID:
40937455
Bibliographic data and abstract were imported from PubMed on 12 Sep 2025.
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