Paired Validation of a Multimodal Vision-Transformer System for the Non-Invasive Diagnosis of Idiopathic Pulmonary Fibrosis.

Omid Tahamtani-Omran¹, Angad Kalra², Michael Muelly², Joshua Reicher², Roaa Ahmed³

¹Department of Chemistry and Biochemistry, University of California, Los Angeles, 607 Charles E. Young Drive East, Los Angeles, CA 90095, USA.

²IMVARIA Inc., 2930 Domingo Ave #1496, Berkeley, CA 94705, USA.

³School of Medicine, Ahfad University for Women, P.O. Box 167, Omdurman 14411, Sudan.

PubMed PMC DOI PDF

Abstract

Background/Objectives: Distinguishing idiopathic pulmonary fibrosis (IPF) from other interstitial lung diseases is prognostically and therapeutically important. We developed Fibresolve, a machine learning system that analyzes chest computed tomography (CT) to support IPF diagnosis. Fibresolve version 1 (v1) was validated in the PUFAIR study, met its co-primary endpoints, and received U.S. FDA auth…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.