# SCH: Smart Breath- based Diagnosis of Pulmonary Exacerbations in Children with Cystic Fibrosis through Machine Learning: Towards Noninvasive Health Monitoring in Real-Time

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $300,000

## Abstract

Cystic fibrosis (CF) is a genetic disorder that negatively affects young people across the globe. Pulmonary
 exacerbations (PEx), episodes of decreased lung function accompanied by coughing, increased sputum
 production, and weight loss, are a hallmark of CF lung disease. Besides negative health impacts, PEx
 comes with a burden to healthcare costs in the United States and beyond ($10K-$40K USD per episode).
 Although PEx can be treated with antibiotics, patients still experience decreased quality of life and
 ultimately reduced survival. A point-of-care, rapid, and accurate test to identify impending PEx that would
 benefit from treatment could have an impact on families of children with CF and their clinicians through
 reducing misdiagnosis and overtreatment. Breath testing, through identifying volatile organic compound
 (VOC) biomarkers, holds great promise for the development of home-based/clinical testing solutions for
 PEx. The goal of this research is to develop a hand-held smart sensor system that can detect exhaled
VOC biomarkers for PEx noninvasively in real-time. To accomplish this, machine learning will be utilized
 to identify a breath-based biosignature of PEx (Aim 1). In parallel, the team will design/test a nanosensor
array to detect the biosignature (Aim 2) and develop a user-friendly smartphone app to be used at-home
 or in the clinic (Aim 3). Ultimately, this research will further the development of diagnostic solutions for
 PEx, and advance knowledge in a multi-faceted fashion across disciplines including basic science,
 chemistry, engineering, medicine, biotechnology, and health informatics. The technological solution is
highly disruptive and challenges the current paradigm of how PEx is diagnosed. From an engineering
 perspective, sensors are at the cusp of being translated into biomedical devices, and this research aims to
overcome challenges in selectivity that can also be leveraged for VOC-based diagnosis of other heart and
lung diseases beyond CF. The interdisciplinary team has vast experience in their respective fields, and
their collaboration ensures successful completion of the research. A diverse set of resources/equipment
from the team's laboratories, along with others on campuses, will be leveraged to support research
activities.
RELEVANCE (See instructions):
 The research addresses

## Key facts

- **NIH application ID:** 11062567
- **Project number:** 1R01HL177812-01
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Mangilal Agarwal
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $300,000
- **Award type:** 1
- **Project period:** 2024-09-09 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11062567

## Citation

> US National Institutes of Health, RePORTER application 11062567, SCH: Smart Breath- based Diagnosis of Pulmonary Exacerbations in Children with Cystic Fibrosis through Machine Learning: Towards Noninvasive Health Monitoring in Real-Time (1R01HL177812-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11062567. Licensed CC0.

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