Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

Journal: Journal of thoracic imaging
Published Date: Nov 2, 2023

Abstract

PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).

Authors

  • Andrew C Lancaster
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Mitchell E Cardin
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Jan A Nguyen
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Tej I Mehta
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Dilek Oncel
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Keira A Cohen
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Cheng Ting Lin
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. clin97@jhmi.edu.

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