Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.

Journal: Clinical imaging
PMID: 38964173

Abstract

PURPOSE: Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE).

Authors

  • Ashish P Wasnik
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Mahmoud M Al-Hawary
    Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Surgery, Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Binu Enchakalody
    Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan, USA.
  • Stewart C Wang
    Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan, MI , USA.
  • Grace L Su
    Michigan Medicine, Department of Internal Medicine, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
  • Ryan W Stidham
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.

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