Deep learning-enabled system for rapid pneumothorax screening on chest CT.

Journal: European journal of radiology
Published Date: Sep 26, 2019

Abstract

PURPOSE: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management. The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image classification program for detection of pneumothorax on chest CT.

Authors

  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • James H Thrall
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. Electronic address: jthrall@mgh.harvard.edu.
  • Subba R Digumarthy
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Mannudeep K Kalra
  • Pari V Pandharipande
    From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Bowen Zhang
    HUB of Intelligent Neuro-Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London, HA7 4LP, UK.
  • Chayanin Nitiwarangkul
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Ramandeep Singh
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Ruhani Doda Khera
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

Keywords

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