Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed Tomography-based Latent Traits: A Deep Learning Approach

Around nine million people had been exposed to toxic humidifier disinfectants (HDs) in Korea. HD-exposure may lead to HD-associated lung injuries (HDLI). However, many people who claimed HD exposure were not diagnosed of HDLI but still felt discomfort possibly due to the unknown effects of HD. Therefore, this study examined HD-exposed subjects with normal appearing lungs as well as unexposed subjects in clusters (subgroups) with distinct characteristics classified by deep-learning-derived computed-tomography (CT)-based tissue-pattern latent traits. Among the major clusters, cluster 0 (C0) and cluster 5 (C5) were dominated by HD-exposed and unexposed subjects, respectively. C0 was characterized by features attributable to lung inflammation or fibrosis compared with C5. The computational fluid and particle dynamics (CFPD) analysis suggested that the smaller airway sizes observed in the C0 subjects led to greater airway resistance and particle deposition in the airways. Accordingly, women appeared more vulnerable to HD-associated lung abnormality than men.