A pilot study of deep learning-based CT volumetry for traumatic hemothorax.

Journal: Emergency radiology
PMID: 35971025

Abstract

PURPOSE: We employ nnU-Net, a state-of-the-art self-configuring deep learning-based semantic segmentation method for quantitative visualization of hemothorax (HTX) in trauma patients, and assess performance using a combination of overlap and volume-based metrics. The accuracy of hemothorax volumes for predicting a composite of hemorrhage-related outcomes - massive transfusion (MT) and in-hospital mortality (IHM) not related to traumatic brain injury - is assessed and compared to subjective expert consensus grading by an experienced chest and emergency radiologist.

Authors

  • David Dreizin
    From the Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine (D.D.), R Adams Cowley Shock Trauma Center, School of Medicine, University of Maryland; Department of Computer Science (Y.Z.), Center for Cognition Vision and Learning, Johns Hopkins University; Diagnostic Radiology and Nuclear Medicine (T.C., G.L.), University of Maryland School of Medicine; Department of Computer Science (A.L.Y.), Center for Cognition Vision and Learning, Johns Hopkins University; Vascular Surgery (A.M., J.J.M.), R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, Maryland.
  • Bryan Nixon
    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Jiazhen Hu
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Benjamin Albert
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Chang Yan
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Gary Yang
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Haomin Chen
    Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Yuanyuan Liang
    Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
  • Nahye Kim
    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Jean Jeudy
    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Elana B Smith
    R. Adams Cowley Shock Trauma Center, University of Maryland Medical Center, Department of Radiology, 22 S. Greene St., Baltimore, MD 21201. Electronic address: elana.smith@umm.edu.
  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.

Keywords

External Resources

Popular Topics
Recent Journals