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

Frank Li, Jiwoong Choi, Xuan Zhang, Prathish K. Rajaraman, Chang Hyun Lee, Hongseok Ko, Kum Ju Chae, Eun Kee Park, Alejandro P. Comellas, Eric A. Hoffman, Ching Long Lin

Research output: Contribution to journalArticlepeer-review

Abstract

Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with 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 in contrast 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 abnormalities than men.

Original languageEnglish
Article number11894
JournalInternational journal of environmental research and public health
Volume19
Issue number19
DOIs
StatePublished - Oct 2022

Keywords

  • cluster analysis
  • computational fluid and particle dynamics
  • computed tomography
  • deep learning
  • humidifier disinfectants

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