Evidence map›Paper›PMID 40937455›Full record

ArticleWorld journal of gastroenterology2025

Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning

Suguru Miida, Hiroteru Kamimura, Shinya Fujiki, Taichi Kobayashi, Saori Endo, Hiroki Maruyama, Tomoaki Yoshida, Yusuke Watanabe, Naruhiro Kimura, Hiroyuki Abe and 13 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Suguru MiidaDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Hiroteru KamimuraDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan. hiroteruk@med.niigata-u.ac.jp.
Shinya FujikiDepartment of Cardiovascular Medicine, Niigata University Medical and Dental Hospital, Niigata 951-8510, Japan.
Taichi KobayashiDivision of Oral and Maxillofacial Radiology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Saori EndoDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Hiroki MaruyamaDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Tomoaki YoshidaDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Yusuke WatanabeDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Naruhiro KimuraDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Hiroyuki AbeDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Akira SakamakiDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Takeshi YokooDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Masanori TsukadaDepartment of Pediatrics, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Fujito NumanoDepartment of Pediatrics, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Takeshi Kashimura
Takayuki InomataDepartment of Cardiovascular Medicine, Niigata University Medical and Dental Hospital, Niigata 951-8510, Japan.
Yuma FuzawaDepartment of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Tetsuhiro HirataDepartment of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Yosuke HoriiDepartment of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Hiroyuki IshikawaDepartment of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan.
Hirofumi NonakaDepartment of Business Administration, Aichi Institute of Technology, Aichi 461-8641, Japan.
Kenya KamimuraDepartment of General Medicine, Niigata University School of Medicine, Niigata 951-8520, Japan.
Shuji TeraiDivision of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCongestive 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.

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

methodsWe 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.

resultsIn 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.

conclusionDeep 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.

Indexed as

Heart FailureLiver DiseasesMachine LearningAgedAged, 80 and overEchocardiographyFemaleGastroenterologistsHumansLiverMaleMiddle AgedRadiologistsRetrospective StudiesSeverity of Illness IndexTomography, X-Ray ComputedArtificial intelligenceHeart failureImage analysisLiver congestionMachine learning

Identifiers

PMID40937455
PMCPMC12421390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.