External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis.

James Bradley¹, Jiapeng Huang², Angad Kalra³, Joshua Reicher⁴

¹Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, Department of Medicine, University of Louisville, Louisville, KY, United States.

²Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, United States.

³Imvaria Inc., Berkeley, CA, United States.

⁴Imvaria Inc., Berkeley, CA, United States. Electronic address: jreicher@imvaria.com.

PubMed DOI

Abstract

BACKGROUND: Previous work has shown the ability of Fibresolve, a machine learning system, to non-invasively classify idiopathic pulmonary fibrosis (IPF) with a pre-invasive sensitivity of 53% and specificity of 86% versus other types of interstitial lung disease. Further external validation for the use of Fibresolve to classify IPF in patients with non-definite usual interstitial pneumonia (UIP) i…

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