Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model.

Journal: Journal of vascular surgery
PMID: 38851467

Abstract

BACKGROUND: Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth.

Authors

  • Jonathan R Krebs
    Department of Surgery, Division of Vascular Surgery, University of Florida, Gainesville, FL.
  • Muhammad Imran
    Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, 54000 Lahore, Pakistan.
  • Brian Fazzone
    Department of Surgery, Division of Vascular Surgery, University of Florida, Gainesville, FL.
  • Chelsea Viscardi
    Department of Surgery, Division of Vascular Surgery, University of Florida, Gainesville, FL.
  • Benjamin Berwick
    Department of Radiology, University of Florida, Gainesville, FL.
  • Griffin Stinson
    Department of Surgery, Division of Vascular Surgery, University of Florida, Gainesville, FL.
  • Evans Heithaus
    Department of Radiology, University of Florida, Gainesville, FL.
  • Gilbert R Upchurch
    TCV Division, Department of Surgery, University of Virginia Medical Center, Charlottesville, Virginia.
  • Wei Shao
  • Michol A Cooper
    Department of Surgery, University of Florida, Gainesville, FL. Electronic address: Michol.cooper@surgery.ufl.edu.

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