Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis

Abstract

Purpose of reviewThe aim of this study was to summarize quantitative computed tomography (CT) and machine learning data in fibrotic lung disease and to explore the potential application of these technologies in pulmonary sarcoidosis.Recent findingsRecent data in the use of quantitative CT in fibrotic interstitial lung disease (ILD) are covered. Machine learning includes deep learning, a branch of machine learning particularly suited to medical imaging analysis. Deep learning imaging biomarker research in ILD is currently undergoing accelerated development, driven by technological advances in image processing and analysis. Fundamental concepts and goals related to deep learning imaging research in ILD are discussed. Recent work highlighted in this review has been performed in patients with idiopathic pulmonary fibrosis (IPF). Quantitative CT and deep learning have not been applied to pulmonary sarcoidosis, although there are recent deep learning data in cardiac sarcoidosis.SummaryPulmonary sarcoidosis presents unsolved problems for which quantitative CT and deep learning may provide unique solutions: in particular, the exploration of the long-standing question of whether sarcoidosis should be viewed as a single disease or as an umbrella term for disorders that might usefully be considered as separate diseases.

Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges

“…Perhaps most importantly, since the computer learns autonomously without explicit programming, an opportunity is created for identifying novel HRCT biomarkers, including those that are not readily identified visually. In respiratory medicine, deep learning has been successfully applied to lung cancer detection, predicting mortality in patients with chronic obstructive pulmonary disease and classifying fibrotic lung disease on CT scans (7,48,51).…”