# Deep Learning and Fluid Dynamics Based Phenotyping of Expiratory Central Airway Collapse

> **NIH NIH R21** · UNIVERSITY OF ALABAMA AT BIRMINGHAM · 2021 · $185,497

## Abstract

Project Summary/ Abstract
Expiratory central airway collapse (ECAC), defined by >50% collapse of large airways during expiration,
resulting from either cartilaginous weakening or redundancy of the posterior membranous wall of the trachea,
is an increasingly recognized disorder associated with cigarette smoking and chronic obstructive pulmonary
disease (COPD). Airflow obstruction in smokers primarily arises from increased resistance to airflow in the
small distal conducting airways <2 mm in diameter. It is plausible that in a subset of smokers with and without
COPD, central airway collapse results in additional resistance to airflow, resulting in substantial respiratory
morbidity.
Ninety-two million adults in the Unites States are active or past smokers, and ECAC is present in
approximately 5% of current and former smokers. The presence of ECAC is associated with greater dyspnea,
worse respiratory-quality of life and greater frequency of exacerbations after adjustment for underlying lung
disease. Whether these patients will benefit from interventional therapies such as stenting or tracheopexy
depends on whether the airflow resistance caused by ECAC contributes to symptoms, and this in turn depends
on the relative contribution of central and small airways to overall airflow resistance. If the overall airflow
resistance is primarily due to distal small airways obstruction in a given patient with ECAC, treating central
airway collapse is unlikely to benefit such a patient. Our central hypothesis is that ECAC results in additional
airflow obstruction beyond that incurred in the small airways, and that in a subset of patients the central
airways are the major site of airflow obstruction and hence are amenable to therapy. The complex interplay of
proximal and distal airway resistances and transpulmonary pressures does not lend itself to direct
measurements in human subjects across a range of physiological pressure and flow changes. We propose a
combination of CT-derived imaging and patient-personalized benchtop model and deep learning to answer
these questions with the following specific aims. Aim 1 of this application will be to derive personalized patient-
specific information on airway geometry and resistance using airway segmentation from computed tomography
(CT) scans. We will calculate airway resistances in central and small airways using standard formulae. The
goal of Aim 2 is to create bench-top simulations to understand the complex interplay between the resistance of
small and large airways. In Aim 3, we will use deep learning to derive probability scores for clinically substantial
ECAC from segmented airway images on computed tomography.
The results of our study will enable patient-specific personalized therapies for ECAC. The mechanistic insights
gained from this study will help identify patients with clinically significant ECAC and hence most likely to benefit
from therapeutic interventions.

## Key facts

- **NIH application ID:** 10149998
- **Project number:** 5R21EB027891-03
- **Recipient organization:** UNIVERSITY OF ALABAMA AT BIRMINGHAM
- **Principal Investigator:** Surya P Bhatt
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $185,497
- **Award type:** 5
- **Project period:** 2019-09-09 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10149998

## Citation

> US National Institutes of Health, RePORTER application 10149998, Deep Learning and Fluid Dynamics Based Phenotyping of Expiratory Central Airway Collapse (5R21EB027891-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10149998. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*