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.

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Title: 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.
Authors: Modanwal G; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA. Electronic address: gourav.modanwal@emory.edu., Al-Kindi S; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA., Walker J; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA., Dhamdhere R; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA., Yuan L; Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China., Ji M; Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China., Lu C; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China., Fu P; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA., Rajagopalan S; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA., Madabhushi A; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
Source: EBioMedicine [EBioMedicine] 2022 Nov; Vol. 85, pp. 104315. Date of Electronic Publication: 2022 Oct 26.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101647039 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2352-3964 (Electronic) Linking ISSN: 23523964 NLM ISO Abbreviation: EBioMedicine Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2352-3964
DOI:10.1016/j.ebiom.2022.104315