How combination of Biofluid simulation and machine learning can help Asthma patients

To improve the effectiveness of asthma inhalers, 360 Engineering BioFluid experts collaborated with pulmonary specialists to develop a digital model to simulate transport of  Nitric Oxide (NO) from lung tissue to the airways in lung and the concentration of NO during exhalation representing inhaler effectiveness.

We used a combination of experimental data and physics-based simulation to shed light on the NO exchange process at different locations in the lung and how it influences the NO concentration which is measurable during the breathing test. The level of NO is often elevated for persons afflicted with inflammatory diseases such as asthma. However, unlike other exhaled gases such as carbon dioxide and nitrogen, NO is unique in its high reactivity with endogenous substrates, its active production from various sources within the body, and its strong dependence on exhalation flow rate. Therefore, pulmonary gas exchange models have been developed by the 360 Degree Engineering team to understand the gas exchange dynamics specifically for NO.

Lung geometry based on realistic data was constructed and imported into simulation software.  Using a non-isotropic diffusion coefficient, we mimic the assumption of the two-compartment model that the concentration of each branch in the airway is uniform at each cross-section. This simulation involves the simultaneous computation of both the Navier-Stokes equation for fluid dynamics and the convection and diffusion equation for mass transfer. The velocity function was determined using information from the experiments done in previous published works. 

The product of our simulation provides the transient NO concentration in airflow for different breathing profiles. This outcome can successfully predict the volume percentage of airways affected by the inflammation, opening the door to improvement of inhalers and other therapeutic interventions. 

With extensive experience in Biofluids, 360 Degree Engineering team provided valuable insight into this process. Currently, we are in the process of adding Machine Learning algorithms to our simulations to reach higher accuracies in this system digital surrogate.

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