Pulmonary contusion: automated deep learning-based quantitative visualization.

Journal: Emergency radiology
PMID: 37318609

Abstract

PURPOSE: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in at-risk trauma patients. This study aims to train and validate state-of-the-art deep learning models to quantify pulmonary contusion as a percentage of total lung volume (Lung Contusion Index, or auto-LCI) and assess the relationship between auto-LCI and relevant clinical outcomes.

Authors

  • Nathan Sarkar
    University of Maryland School of Medicine, Baltimore, MD, USA.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Peter Campbell
    Department of Ophthalmology, Oregon Health and Science University, Portland, OR.
  • Yuanyuan Liang
    Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Mustafa Khedr
    Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA.
  • Udit Khetan
    Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA.
  • 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.

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