Dosimetry Sensitivity in a Lower Dimensional Model of Patient-Specific Asthma Subjects

Objective: Experimental uncertainty will impact
in silico
model calculations of aerosol delivery and deposition. Patient-specific dosimetry models are often parameterized based on medical imaging data, which contain inherent experimental variability. Methods: Here, we created and parameterized 1D models of three subject-specific asthmatic subjects and randomly assigned perturbations of up to 15
$\%$
on airway diameter, segmental volume, and defected volume. Sensitivity of imaging data experimental variability on dosimetry metrics were quantified. Results: Lobar particle delivery primarily depended on the distal segmental volumes; 15
$\%$
range of noise resulted in delivery to the upper right lobe to vary at most from 15.2 and 18.2
$\%$
for one of the severe subjects. Particle deposition was most sensitive to airway diameter; 95
$\%$
confidence intervals spanned from 8 to 10.6
$\%$
in the mild/moderate subject for 15
$\%$
variation on input metrics for 5
$\mu \text{m}$
diameter particles. While these results provide possible ranges of dosimetry calculations for a specific subject, the perturbations were not sufficient to model the large observed inter-subject variability (8.9, 19, and 14.5
$\%$
deposition, subjects 1--3, respectively, 5
$\mu \text{m}$
diameter particles). Conclusion: This study highlights that
in silico
model predictions are robust in the presence of experimental uncertainty and that it continues to be necessary to perform subject-specific simulations, especially within the presence of heterogeneous airway disease. Significance: Sensitivity analysis provides confidence in calculating deposition in the airways of asthmatic subjects within the presence of experimental uncertainty.