Automated artificial intelligence detection of early or under-diagnosed interstitial lung disease by computed tomography in the COPDGene trial.

Stephanie J Chen¹, Angad Kalra², Michael Muelly³, Joshua Reicher⁴, Sean Callahan⁵, Mary Beth Scholand⁶, Tejaswini Kulkarni⁷

¹Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA. Electronic address: sjichen@stanford.edu.

²IMVARIA Inc., 1748 Shattuck Ave, Berkeley, CA, 94709, USA. Electronic address: angad@imvaria.com.

³IMVARIA Inc., 1748 Shattuck Ave, Berkeley, CA, 94709, USA. Electronic address: mmuelly@imvaria.com.

⁴IMVARIA Inc., 1748 Shattuck Ave, Berkeley, CA, 94709, USA. Electronic address: jreicher@imvaria.com.

⁵Division of Pulmonary Diseases and Critical Care Medicine, University of North Carolina School of Medicine, 321 S Columbia St, Chapel Hill, NC, 27599, USA. Electronic address: sean_callahan@med.unc.edu.

⁶Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah, 26 South 2000 East, Salt Lake City, UT, 84112, USA. Electronic address: mary.beth.scholand@hsc.utah.edu.

⁷Department of Pulmonology, Allergy, and Critical Care Medicine, The University of Alabama at Birmingham, 1720 2nd Ave South, Birmingham, AL, 35294, USA. Electronic address: tkulkarni@uabmc.edu.

PubMed DOI

Abstract

INTRODUCTION: Diagnostic delays are common in interstitial lung disease (ILD) and there is a need for improved detection methods for early clinical ILD detection. ScreenDx is an artificial intelligence tool that assesses computed tomography (CT) scans for interstitial lung findings compatible with ILD. We investigated the ability of ScreenDx to identify ILD cases in the COPDGene dataset that were…

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