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

Frank Li; Jiwoong Choi; Xuan Zhang; Prathish Rajaraman; Chang-Hyun Lee; Hongseok Ko; Kum-Ju Chae; Eun-Kee Park; Alejandro Comellas; Eric Hoffman; Ching-Long Lin

doi:10.3390/ijerph191911894

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

Journal article

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

Frank Li, Jiwoong Choi, Xuan Zhang, Prathish Rajaraman, Chang-Hyun Lee, Hongseok Ko, Kum-Ju Chae, Eun-Kee Park, Alejandro Comellas, Eric Hoffman, …

International journal of environmental research and public health, Vol.19(19), 11894

01/01/2022

DOI: 10.3390/ijerph191911894

PMCID: PMC9565839

PMID: 36231196

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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.

Asthma

Computed Tomography

Computer Applications

Medical Imaging

Software

Abnormalities

Antiseptics

Chronic obstructive pulmonary disease

Clusters

Deep learning

Disinfectants

Disinfection & disinfectants

Dynamic tests

Exposure

Fibrosis

Lungs

Particle deposition

Respiratory tract

Subgroups

Tomography

Details

Title: Subtitle: Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach
Creators: Frank Li - University of Iowa
Jiwoong Choi
Xuan Zhang
Prathish Rajaraman
Chang-Hyun Lee
Hongseok Ko
Kum-Ju Chae
Eun-Kee Park
Alejandro Comellas
Eric Hoffman
Ching-Long Lin
Resource Type: Journal article
Publication Details: International journal of environmental research and public health, Vol.19(19), 11894
DOI: 10.3390/ijerph191911894
PMID: 36231196
PMCID: PMC9565839
NLM abbreviation: Int J Environ Res Public Health
ISSN: 1661-7827
eISSN: 1660-4601
Publisher: MDPI AG
Grant note: name: NIH, award: U01-HL114494, R01-HL112986 and S10-RR022421, T32-HL-144461, 2018001360001; DOI: 10.13039/501100003725, name: National Research Foundation of Korea, award: NRF-2017R1D1A1B03034157
Language: English
Date published: 01/01/2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; ICTS; IIHR--Hydroscience and Engineering; Mechanical Engineering; Internal Medicine
Record Identifier: 9984306258602771

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