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Authors
Tang, An https://orcid.org/0000-0001-8967-5503
Destrempes, François
Kazemirad, Siavash
Garcia-Duitama, Julian
Nguyen, Bich N.
Cloutier, Guy
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Funding
Funding for this research was provided by:
Fonds de Recherche du Québec - Nature et Technologies (PR-174387)
Institute of Nutrition, Metabolism and Diabetes (273738 and 301520)
Centre de Recherche du Centre Hospitalier de l'Université de Montréal
Fonds de Recherche du Québec en Santé and Fondation de l'association des radiologistes du Québec (34939)
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Article History
Received: 31 July 2018
Revised: 29 October 2018
Accepted: 23 November 2018
First Online: 17 December 2018
Compliance with ethical standards
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: The scientific guarantor if this publication is An Tang.
: The authors declare that they have no conflict of interest.
: One of the authors has significant statistical expertise.
: Approval from the institutional animal care committee was obtained.
: Institutional review board approval was obtained.
: The study cohorts have been previously reported in []. That prior article focused on a comparison between low-frequency versus high-frequency ultrasonographic shear wave elastography for the detection of steatohepatitis. In contrast, the present article investigates a machine learning model based on quantitative ultrasound parameters to improve classification of steatohepatitis compared to shear wave elastography alone.
: • Diagnostic study• Experimental• Performed at one institution
