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

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 initially undiagnosed to assess the tool's performance in detecting early or undiagnosed ILD.

Methods: The COPDGene trial was a NIH registered study assessing genetic factors in COPD. ILD was an exclusion criterion, however some ILD patients were unintentionally included initially and subsequently re-labeled by investigators. These patients were selected as "positives" for the study. Additional COPD and control patients were randomly selected from the dataset as "negatives" for the study to achieve a target ILD prevalence of ∼1-2 % for the cohort. ScreenDx is a deep learning model designed to detect features of ILD on CT. CT scans from the study cohort were processed through ScreenDx, testing for sensitivity and specificity.

Results: At the previously selected and optimized threshold, ScreenDx demonstrated a sensitivity of 84.8 % (95th CI: 68.1-94.9 %) and specificity of 98.0 % (95th CI: 97.3-98.5 %) for automatically detecting ILD in the study cohort.

Conclusion: ScreenDx successfully detected 84.8 % of clinically significant ILD that were underdiagnosed and intended to be excluded from the COPDGene trial, while maintaining high specificity. It holds promise as an efficient method for identifying early or under-diagnosed ILD automatically.