ArticleEBioMedicine2022
Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.
Article in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- Five-year cardiovascular outcomes following COVID-19-associated carditis.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.Nature communications · 2026Article
- QuLF-CT: A Radiomics-Based Tool for Quantification of Liver Fat Fraction on Cardiac CT.JACC. Advances · 2024Article
- Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.Scientific reports · 2024Article
- Longitudinal medical subspecialty follow-up of critically and non-critically ill hospitalized COVID-19 survivors up to 24 months after discharge.Internal and emergency medicine · 2023Article
- Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.Frontiers in medicine · 2023Article
- Immunohematologic Biomarkers in COVID-19: Insights into Pathogenesis, Prognosis, and Prevention.Pathogens & immunity · 2023Review
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10 authors.
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Abstract
backgroundHepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.
methodsDeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity.
findingsThe DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001).
interpretationThe DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment.
fundingFor a full list of funding bodies, please see the Acknowledgements.
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