Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach
Abstract
:1. Introduction
2. Methods
2.1. Human Subject Data and Image Processing
2.2. 3D Convolutional Autoencoder (CAE) and Feature Constructor (FC)
2.3. Factor Interpretation
2.4. Identification of the Subject Clusters
2.5. Simulations of Airflow in the Airways Using CFPD
2.6. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BMI | Body mass index |
PFT | Pulmonary function test |
EX | Expiration |
IN | Inspiration |
FEV1 | Forced expiratory volume in one second |
FVC | Forced vital capacity |
HD | Humidifier disinfectant |
HDLI | HD-associated lung injuries |
CT | Computed tomography |
EFA | Exploratory factor analysis |
IRB | Institutional review boards |
ROI | Regions of interest |
CAE | Convolutional autoencoder |
FC | Feature constructor |
AWV% | Airway tree to lung volume ratio |
RV/TLC | Residual volume to total lung capacity ratio |
CFPD | Computational fluid and particle dynamics |
LES | Large eddy simulation |
F0 | Factor 0 |
F1 | Factor 1 |
F2 | Factor 2 |
F3 | Factor 3 |
F4 | Factor 4 |
F5 | Factor 5 |
C0 | Cluster 0 |
C1 | Cluster 1 |
C2 | Cluster 2 |
C3 | Cluster 3 |
C4 | Cluster 4 |
C5 | Cluster 5 |
Variables below measured at different locations are denoted by {Variable}_{Location}. | |
Variable | |
Dh | Hydraulic luminal diameter |
LAARV% | Low attenuation area percentage at residual volume |
LAATLC% | Low attenuation area percentage at total lung capacity |
fSAD% | Functional small airway disease percentage |
J | Determinant of the Jacobian matrix |
Location | |
LUL | Left upper lobe |
LLL | Left lower lobe |
RUL | Right upper lobe |
RML | Right middle lobe |
RLL | Right lower lobe |
Total | Total lung |
sLUL | Sub-lobar subset airways at LUL |
sLLL | Sub-lobar subset airways at LLL |
sRUL | Sub-lobar subset airways at RUL |
sRML | Sub-lobar subset airways at RML |
sRLL | Sub-lobar subset airways at RLL |
lLUL | Lobar airway at LUL |
lLLL | Lobar airway at LLL |
Lobar airway at RUL | Lobar airway at RUL |
Lobar airway at RML | Lobar airway at RML |
Lobar airway at RLL | Lobar airway at RLL |
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Exposed (n = 96) | Non-Exposed (n = 25) | Total (n = 121) | p | |
---|---|---|---|---|
Age (yrs.) | 49.86 | 45.44 | 48.95 | 0.116 |
(15.43) | (11.38) | (14.75) | ||
BMI (kg/cm2) | 23.40 | 24.72 | 23.67 | 0.057 |
(3.21) | (2.94) | (3.19) | ||
Height (cm) | 163.95 | 169.96 | 165.20 | 0.002 |
(7.89) | (8.15) | (8.28) | ||
Weight (kg) | 63.11 | 71.76 | 64.91 | 0.003 |
(10.94) | (12.06) | (11.68) | ||
FVC (% of pred) | 87.16 | 99.28 | 89.68 | <0.001 |
(16.64) | (7.17) | (15.92) | ||
FEV1 (% of pred) | 89.53 | 107.72 | 93.32 | <0.001 |
(20.86) | (10.51) | (20.52) | ||
Gender (%) | 45.8/54.2 | 16.0/84.0 | 39.7/60.3 | 0.013 |
(Female/Male) |
C0 | C1 | C2 | C5 | p | |
---|---|---|---|---|---|
Age (yrs.) | 54.41 | 48.96 | 40.89 | 49.91 | 0.005 |
(15.55) | (14.43) | (12.03) | (14.36) | ||
BMI (kg/cm2) | 23.74 | 25.14 | 22.29 | 23.65 | 0.038 |
(3.19) | (2.69) | (3.37) | (3.03) | ||
Height (cm) | 161.55 | 163.35 | 166.96 | 168.33 | <0.001 |
(8.40) | (7.44) | (8.32) | (7.61) | ||
Weight (kg) | 62.16 | 67.43 | 62.65 | 67.25 | 0.142 |
(10.81) | (11.12) | (13.24) | (10.96) | ||
FVC (% pred) | 81 | 93.7 | 94.75 | 91 | 0.104 |
(18.34) | (16.74) | (14.60) | (11.17) | ||
FEV1 (% pred) | 82.58 | 98.48 | 101.18 | 94.76 | 0.064 |
(24.40) | (17.38) | (13.86) | (18.15) | ||
Gender (%) | 59.4/40.6 | 52.2/47.8 | 39.3/60.7 | 15.2/84.8 | <0.001 |
(Female/Male) | |||||
Exposure (%) | 96.9/3.1 | 87.0/13.0 | 82.1/17.9 | 51.5/48.5 | <0.001 |
(Yes/No) | |||||
Time of Exposure (hrs.) | 15,075.72 | 11,293.16 | 10,459.82 | 15,928.53 | 0.61 |
(18,150.32) | (10,507.95) | (10,561.51) | (21,145.16) | ||
PHMG or PGH (Count) | 17 | 14 | 13 | 9 | 0.65 |
CMIT or MIT (Count) | 1 | 2 | 1 | 0 | |
PHMG AND CMIT (Count) | 9 | 2 | 5 | 6 | |
Other HDs (Count) | 2 | 1 | 3 | 2 |
Exposure | No | Yes | Total |
---|---|---|---|
Cluster | |||
C0 | 1 | 31 | 32 |
C1 | 3 | 20 | 23 |
C2 | 5 | 23 | 28 |
C3 | 0 | 1 | 1 |
C4 | 0 | 4 | 4 |
C5 | 16 | 17 | 33 |
C0 | C1 | C2 | C5 | |
---|---|---|---|---|
Height | -- | -- | - | |
BMI | + | |||
FVC % pred | ||||
FEV1 % pred | ||||
RV/TLC | + | - | - | |
RV | - | - | - | |
TLC | --- | |||
LAARV% | - | -- | -- | |
fSAD% | - | -- | -- | |
Tissue% | +++ | ++ | ||
LAATLC% | -- | -- | - | |
F1 | +++ | |||
F2 | -- | +++ | ||
F3 | --- | +++ | ||
F5 | +++ |
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Li, F.; Choi, J.; Zhang, X.; Rajaraman, P.K.; Lee, C.-H.; Ko, H.; Chae, K.-J.; Park, E.-K.; Comellas, A.P.; Hoffman, E.A.; et al. Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach. Int. J. Environ. Res. Public Health 2022, 19, 11894. https://doi.org/10.3390/ijerph191911894
Li F, Choi J, Zhang X, Rajaraman PK, Lee C-H, Ko H, Chae K-J, Park E-K, Comellas AP, Hoffman EA, et al. Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach. International Journal of Environmental Research and Public Health. 2022; 19(19):11894. https://doi.org/10.3390/ijerph191911894
Chicago/Turabian StyleLi, Frank, Jiwoong Choi, Xuan Zhang, Prathish K. Rajaraman, Chang-Hyun Lee, Hongseok Ko, Kum-Ju Chae, Eun-Kee Park, Alejandro P. Comellas, Eric A. Hoffman, and et al. 2022. "Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach" International Journal of Environmental Research and Public Health 19, no. 19: 11894. https://doi.org/10.3390/ijerph191911894