Evaluation of semi-automated versus fully automated technologies for computed tomography scalable body composition analyses in patients with severe acute respiratory syndrome Coronavirus-2.

Journal: Clinical nutrition ESPEN
Published Date: Jun 11, 2025

Abstract

RATIONALE AND OBJECTIVES: Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal was to compare the results of a fully automated, AI-based software with a semi-automatic software in a sample of hospitalized patients.

Authors

  • Amy Wozniak
    Loyola University Chicago, 2160 South First Avenue, Building 115, Maywood, IL 60153, USA. Electronic address: awozniak@luc.edu.
  • Paula O'Connor
    Loyola University Chicago, Parkinson School of Health Science and Public Health, 2160 South First Avenue, Cuneo 4th Floor Suite, Maywood, IL 60153, USA. Electronic address: poconnor1@luc.edu.
  • Jared Seigal
    Loyola University Chicago, Parkinson School of Health Science and Public Health, 2160 South First Avenue, Maywood, IL 60153, USA. Electronic address: jseigal@luc.edu.
  • Vasilios Vasilopoulos
    Loyola University Medical Center, Department of Radiology, 2160 South First Avenue, Maywood, IL 60153, USA. Electronic address: vvasilopoulos@lumc.edu.
  • Mirza Faisal Beg
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.
  • Karteek Popuri
    Simon Fraser University, School of Engineering Science, Burnaby BC V5A 1S6, Canada.
  • Cara Joyce
    Loyola University Chicago, Chicago, IL.
  • Patricia Sheean
    Loyola University Chicago, Parkinson School of Health Science and Public Health, 2160 South First Avenue, Cuneo 439, Maywood, IL 60153, USA. Electronic address: psheean1@luc.edu.

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