ScreenDx, an artificial intelligence-based algorithm for the incidental detection of pulmonary fibrosis.

Journal: The American journal of the medical sciences
Published Date: Feb 27, 2025

Abstract

BACKGROUND: Nonspecific symptoms and variability in radiographic reporting patterns contribute to a diagnostic delay of the diagnosis of pulmonary fibrosis. An attractive solution is the use of machine-learning algorithms to screen for radiographic features suggestive of pulmonary fibrosis. Thus, we developed and validated a machine learning classifier algorithm (ScreenDx) to screen computed tomography imaging and identify incidental cases of pulmonary fibrosis.

Authors

  • Nikolas Touloumes
    Division of General Internal Medicine, Dept. of Medicine, University of Louisville. 550 South Jackson Street, 3rd Floor, Ste A3K00, Louisville, KY 40202, United States.
  • Georgia Gagianas
    Philadelphia College of Osteopathic Medicine. Philadelphia, PA, 4170 City Avenue, Philadelphia, PA 19131, United States.
  • James Bradley
    Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, Dept. of Medicine, University of Louisville. 550 South Jackson Street, 3rd Floor, Ste A3R40, Louisville, KY 40202, United States. Electronic address: james.adam.bradley@gmail.com.
  • Michael Muelly
    Imvaria, Inc, USA.
  • Angad Kalra
    Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. Electronic address: angadk@cs.toronto.edu.
  • Joshua Reicher
    Department of Radiology, Palo Alto VA Medical Center, Palo Alto, California.

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