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Peer-Reviewed Publication
Am J Med Sci2025;369(6):705-711.June 1, 2025Journal Article

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

Nikolas Touloumes1, Georgia Gagianas2, James Bradley3, Michael Muelly4, Angad Kalra5, Joshua Reicher5
1Division of General Internal Medicine, Dept. of Medicine, University of Louisville. 550 South Jackson Street, 3rd Floor, Ste A3K00, Louisville, KY 40202, United States.
2Philadelphia College of Osteopathic Medicine. Philadelphia, PA, 4170 City Avenue, Philadelphia, PA 19131, United States.
3Division 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.
4Division of Body MRI, Department of Radiology, Stanford Medicine Diagnostic Radiology, 300 Pasteur Dr Rm S092, MC 5105, Stanford, CA 94305, United States; Imvaria Inc. Berkeley, CA 94709, United States.
5Imvaria Inc. Berkeley, CA 94709, United States.
PubMed DOI

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 tomog…

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