# Imaging biomarkers of severe respiratory infections in premature infants Phase II

> **NIH NIH R42** · KITWARE, INC. · 2021 · $852,101

## Abstract

ABSTRACT
 Prematurity is the largest single cause of death in children under five in the world and lower respiratory tract
infections (LRTI) are the top cause of hospitalization and mortality in premature infants. Clinical tools to predict
the risk and assess the severity of LRTI in premature babies are critically needed to allow early interventions to
decrease the high morbidity and mortality in this patient group. Our goal is to improve clinical practice by
developing an objective framework to predict the risk and assess the severity of respiratory disease in premature
babies using non-invasive low-radiation X-ray imaging biomarkers and clinical parameters.
 In the Phase I of this project, our multidisciplinary team of pulmonologists, neonatologists and imaging and
machine learning specialists developed an imaging software technology called Lung Aeration and Irregular
opacities Radiological analyzer (LungAIR). Our accomplishments include: 1) establishing a curated ground truth
of focal findings in chest X-Ray (CXR) of premature babies; 2) developing a machine learning algorithm to
automatically localize and quantify CXR-based prematurity lung disease signatures (fibrosis/interstitial opacities,
cystic changes and hyperinflation); 3) creating a graphical user interface for clinical deployment; and 4)
evaluating our imaging software technology in an independent cohort. We also demonstrated that the imaging
biomarkers obtained by LungAIR correlate strongly with the severity of bronchopulmonary dysplasia (BPD)—the
most common respiratory complication of prematurity-- and the cumulative exposure to supplemental O2 and
mechanical ventilation in the neonatal intensive care unit (NICU) (p<0.001). Importantly, our preliminary results
indicated that the combination of imaging and clinical markers (BPD severity) provide an accurate predictive
model for LRTI-related complications in the first year of life (AUC=74, p<0.01).
 This Phase II project builds on the findings and methodology developed in Phase I. In Specific Aim 1, we will
incorporate a model of lung disease risk factors in LungAIR platform. Our software will ingest respiratory support
information daily during NICU hospitalization and integrate the data with CXR analysis. In Specific Aim 2, we will
extend LungAIR to perform longitudinal analyses during hospitalization with the potential to accelerate the
prediction of health risks. We will also integrate our results with the electronic health record of the patient for
improve the clinical workflow. In Specific Aim 3 we will conduct a clinical study to prospectively evaluate the
LungAIR clinical platform functionality. The proposal includes the business model and a path to commercializing
LungAIR. The early identification of premature babies at high risk for BPD and severe LRTI should improve their
outcome, reduce hospitalization times and inherent clinical costs, and decrease infant mortality. In addition, the
ability to objectively quantify and t...

## Key facts

- **NIH application ID:** 10137685
- **Project number:** 2R42HL145669-02A1
- **Recipient organization:** KITWARE, INC.
- **Principal Investigator:** Andinet Asmamaw Enquobahrie
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $852,101
- **Award type:** 2
- **Project period:** 2018-05-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10137685

## Citation

> US National Institutes of Health, RePORTER application 10137685, Imaging biomarkers of severe respiratory infections in premature infants Phase II (2R42HL145669-02A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10137685. Licensed CC0.

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