AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

Journal: Abdominal radiology (New York)
PMID: 38280049

Abstract

PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events.

Authors

  • Matthew H Lee
    Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792.
  • Ryan Zea
    Department of Radiology, The University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
  • John W Garrett
    From the Departments of Medical Physics (R.Z., X.T., C.Z., D.G., J.W.G., K.L., S.B.R., G.H.C.) and Radiology (M.L.S., J.W.G., K.L., S.B.R., G.H.C.), University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Ave, Madison, WI 53705; and Department of Radiology, Henry Ford Health System, Detroit, Mich (Z.Q., N.B.B., T.K.S., J.D.N,).
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.

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