Jacob, J; (2019) Quantitative CT analysis in ILD and use of artificial intelligence on imaging of ILD. ERS Monograph: Pulmonary Manifestations of Systemic Diseases , 86 pp. 27-43. 10.1183/2312508X.erm8619.

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Abstract

Advances in computer technology over the past decade, particularly in the field of medical image analysis, have permitted the identification, characterisation and quantitation of abnormalities that can be used to diagnose disease or determine disease severity. On CT imaging performed in patients with ILD, deep-learning computer algorithms now demonstrate comparable performance with trained observers in the identification of a UIP pattern, which is associated with a poor prognosis in several fibrosing ILDs. Computer tools that quantify individual voxel-level CT features have also come of age and can predict mortality with greater power than visual CT analysis scores. As these tools become more established, they have the potential to improve the sensitivity with which minor degrees of disease progression are identified. Currently, PFTs are the gold standard measure used to assess clinical deterioration. However, the variation associated with pulmonary function measurements may mask the presence of small but genuine functional decline, which in the future could be confirmed by computer tools. The current chapter will describe the latest advances in quantitative CT analysis and deep learning as related to ILDs and suggest potential future directions for this rapidly advancing field.

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