Perspectives on physics-based one-dimensional modeling of lung physiology

TL;DR

This paper reviews one-dimensional physics-based models of lung physiology, highlighting their successes, limitations, and potential for clinical translation in understanding lung diseases and guiding treatments.

Contribution

It consolidates current 1D lung models, discusses their applications in disease understanding, and emphasizes future directions for clinical integration and personalized medicine.

Findings

1D trumpet models provide mechanistic insights into lung function.

Non-dimensional numbers characterize lung physiology and pathology.

Potential for 1D models to aid in clinical diagnosis and treatment planning.

Abstract

The need to understand how infection spreads to the deep lung was acutely realized during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic. The challenge of modeling virus laden aerosol transport and deposition in the airways, coupled with mucus clearance, and infection kinetics, became evident. This perspective provides a consolidated view of coupled one-dimensional physics-based mathematical models to probe multifaceted aspects of lung physiology. Successes of 1D trumpet models in providing mechanistic insights into lung function and optimalities are reviewed while identifying limitations and future directions. Key non-dimensional numbers defining lung function are reported. The need to quantitatively map various pathologies on a physics-based parameter space of non-dimensional numbers (a virtual disease landscape) is noted with an eye on translating modeling to clinical practice. This could aid in disease diagnosis, get mechanistic insights into pathologies, and determine patient specific treatment plan. 1D modeling could be an important tool in developing novel measurement and analysis platforms that could be deployed at point-of-care.