Evidence map›Paper›PMID 42480641›Full record

ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2026

Automated Deep Learning Detection of Hepatic Steatosis on Noncontrast Computed Tomography Scans and Discrepancy With Scan Reports.

David Yardeni, Jianfei Liu, Pritam Mukherjee, Thomas C Shen, Daniel C Elton, Christopher Parnell, Winnie Trang, Wesam Aleyadeh, Gian Rodriguez Franco, Sungwon Lee and 2 more

Abstract read
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Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

David YardeniLiver and Energy Metabolism Section, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland; Department of Gastroenterology and Liver Diseases, Soroka University Medical Center, Beersheba, Israel.
Jianfei LiuImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Pritam MukherjeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Thomas C ShenImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Daniel C EltonImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Christopher ParnellDiagnostic Radiology, Walter Reed National Military Medical Center, Bethesda, Maryland.
Winnie TrangBiomedical Translational Research Information System, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Wesam AleyadehLiver and Energy Metabolism Section, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
Gian Rodriguez FrancoLiver and Energy Metabolism Section, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
Sungwon LeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, National Institutes of Health, Bethesda, Maryland.
Yaron RotmanLiver and Energy Metabolism Section, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland. Electronic address: rotmany@niddk.nih.gov.

Funding

Nonalcoholic Steatohepatitis: Natural History, Pathogenesis and TherapyZIADK075013 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI ROTMAN, YARON · 2009 to 2025
$19.1M
Computer Aided Detection for Radiologic ImagesZ01CL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2003 to 2008
$41k
Intramural NIH HHS Z01 CL040004Intramural NIH HHS Z99 DK999999Intramural NIH HHS ZIA DK075013
6 · The paper itself

Abstract

BACKGROUND &

aimsMetabolic dysfunction-associated steatotic liver disease is a major cause of liver disease that is growing in prevalence. Typically asymptomatic, is often undiagnosed. Imaging studies, including noncontrast computed tomography (CT) scans, can detect hepatic steatosis opportunistically, but the reporting rate is unknown. We hypothesized that incidental finding of steatosis on noncontrast CT is often not reported if not specifically sought in the imaging request.

methodsThis study was a retrospective, cross-sectional, single-center analysis of abdominal noncontrast CT scans performed between 2012 and 2020 for any indication in adult subjects. Images were analyzed using an automated deep learning liver segmentation and attenuation assessment algorithm to obtain a mean volumetric liver attenuation value. Image-based steatosis was defined as mean hepatic attenuation <40 HU and compared with textual radiology reports. Manual review of a random subset of scans and reports was used to verify results.

resultsA total of 3646 noncontrast CT scans from 2710 adult patients were analyzed. The mean liver attenuation derived from the deep-learning algorithm was 50.4 ± 11.8 HU. Image-based steatosis was found in 480 (13.1%) scans, with a mean liver attenuation of 29.8 ± 14 HU. Radiologists reported steatosis in only 157 (32.7%) of these low-attenuation scans. Predictors of unreported steatosis included higher average liver attenuation (even if < 40 HU), high variability of fat distribution in the liver and low body mass index.

conclusionsWe found that incidental hepatic steatosis in noncontrast CT is reported in a minority of scans. Incorporating artificial intelligence-based hepatic attenuation measurement in computed tomography scan reading may increase reporting rates.

Indexed as

AttenuationComputerized TomographyDeep LearningRadiologySteatosis

Identifiers

PMID42480641
PMCPMC13550851

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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.