Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

Journal: Pharmacoepidemiology and drug safety
Published Date: Jun 8, 2023

Abstract

PURPOSE: Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status.

Authors

  • Jessie Torgersen
    Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Scott Akers
    Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
  • Yuankai Huo
    Vanderbilt University, Nashville, TN 37212, USA.
  • James G Terry
    Vanderbilt University Medical Center, , Nashville, USA.
  • J Jeffrey Carr
    Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
  • Alexander T Ruutiainen
    Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
  • Melissa Skanderson
    Connecticut VA Healthcare System, West Haven, CT, USA.
  • Woody Levin
    Roxanne Wadia, Kathleen Akgun, Cynthia Brandt, Brenda T. Fenton, Andrew H. Marple, Vijay Garla, Michal G. Rose, and Tamar Taddei, Yale University School of Medicine, New Haven; and Roxanne Wadia, Kathleen Akgun, Cynthia Brandt, Brenda T. Fenton, Woody Levin, Michal G. Rose, Tamar Taddei, and Caroline Taylor, Veterans Affairs Connecticut Healthcare System, West Haven, CT.
  • Joseph K Lim
    Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Tamar H Taddei
    Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Kaku So-Armah
    Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.
  • Debika Bhattacharya
    VA Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
  • Christopher T Rentsch
    VA Connecticut Healthcare System, West Haven, CT, USA.
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Rotonya Carr
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Russell T Shinohara
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Michele McClain
    VA Office of Information and Technology, Frederick, Maryland, USA.
  • Matthew Freiberg
    Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
  • Amy C Justice
    Department of Internal Medicine, Yale University School of Medicine, New Haven.
  • Vincent Lo Re
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

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