# A Pediatric Atlas of Upper Airway Shape

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $1

## Abstract

ABSTRACT
Airway abnormalities in children, such as subglottic stenosis (SGS) and Robin sequence (PRS), may result in
breathing difficulties, risk of recurrent infections, hypoxia, respiratory insufficiency, life-threatening events, and
long-term morbidity. In children with airway abnormalities, a multidisciplinary approach to care involves
selection from a variety of medical and surgical interventions. Therapy is typically directed by the clinician's
experience and preference, rather than based on normalized, quantitative physiologic and anatomic metrics.
Static computed tomography (CT), dynamic CT, and bronchoscopy have been considered for quantitative
diagnosis and assessment. However, quantitative measures of what constitutes normal airway geometry and
how normal airway geometry changes with respect to age, weight, and sex are lacking. Such normative
measures can be used to score the degree of airway abnormality, define thresholds for abnormality, and better
understand surgical interventions' impact. In previous work, we developed the Pediatric Airway Atlas to provide
spatially localized normative measures for upper airway cross-sectional areas in children derived from a
population of static 3D CT images.
The goal of the proposed study is to build upon our database of 3D CT images and associated clinical
measures to develop the computational methodology for a Pediatric Airway Shape Atlas (PASA), which will
model the upper airway as a 3D shape instead of restricting airway characterization to cross-sectional area
only. The PASA will allow for a comprehensive characterization of 3D geometry. Specifically, the core of the
PASA will be a new, innovative neural additive shape model that is designed to allow for interpretable results,
captures the effects of relevant covariates (such as age, sex, and weight), and allows within the same
framework to predict likely airway changes over time for individuals thereby providing a means to quantify the
effect of surgical interventions on 3D airway geometry.
Our approach will provide improved, non-invasive quantification of airway abnormalities. Automated data
analysis will allow for rapid refinement of atlas-based analyses and will greatly simplify use by other research
and clinical groups. The resulting software will be open-source. Furthermore, the new methodologies
developed will be broadly applicable to multiple, common causes of airway obstruction in children and adults.

## Key facts

- **NIH application ID:** 10976221
- **Project number:** 1R21HL172230-01A1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Marc Niethammer
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1
- **Award type:** 1
- **Project period:** 2024-08-01 → 2024-12-23

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10976221

## Citation

> US National Institutes of Health, RePORTER application 10976221, A Pediatric Atlas of Upper Airway Shape (1R21HL172230-01A1). Retrieved via AI Analytics 2026-06-25 from https://api.ai-analytics.org/grant/nih/10976221. Licensed CC0.

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