Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

Journal: The journal of trauma and acute care surgery
PMID: 32107356

Abstract

INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative visualization and measurement algorithm predict interventions and outcomes including (a) need for angioembolization (AE), pelvic packing (PP), or massive transfusion (MT), and (b) in-hospital mortality.

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.
  • Yuyin Zhou
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Tina Chen
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Alan L Yuille
  • Ashley McLenithan
  • Jonathan J Morrison

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