Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach
Year: 2022
Type: article
Study design: Observational Study
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.
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,618
Funders: National Research Foundation, Ministry of Environment, National Research Foundation of Korea, Ministry of Education, India, National Institutes of Health, Korea Environmental Industry and Technology Institute, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences, National Center for Research Resources
Awards: G1476458040, Iowa Lung Imaging Training Program, An integrative statistics-guided image-based multi-scale lung model, G3148290576, G3782424196, G4085622990, G4582554841, G497218177, Multi-center Structural & Functional Quantitative CT Pulmonary Phenotyping, G5607417404, G6001878612, G7204526335, G7574538364, G7600354450, Large-Scale Computing and Visualization for Cardiopulmonary Imaging, Environmental Health Sciences Research Center, Precision Medicine Center for Cystic Fibrosis
OpenAlex ID: W4296745802