SBIR Phase II: An AI-powered wearable system and platform for long-term remote monitoring of pulmonary function (COVID-19)
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2112096
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Key facts
Disease
COVID-19Start & end year
20212023Known Financial Commitments (USD)
$958,275Funder
National Science Foundation (NSF)Principal Investigator
Maria ArtunduagaResearch Location
United States of AmericaLead Research Institution
Respira Labs IncResearch Priority Alignment
N/A
Research Category
Clinical characterisation and management
Research Subcategory
Supportive care, processes of care and management
Special Interest Tags
Digital Health
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve care for pulmonary conditions, such as Chronic Obstructive Pulmonary Disease (COPD). This disease affects 30 million Americans, kills more than 150,000 a year and is the third leading cause of U.S. deaths. COPD costs Americans nearly $72 billion a year, half of which is spent on ER visits and hospital stays resulting from respiratory crises. Hospitalized COPD patients have reduced quality of life, more rapid lung disease progression, and a 50% increase in mortality in the subsequent two years. Currently, ongoing monitoring of COPD lung deterioration relies largely on patient-reported symptoms and inadequate tools (65-70% accurate). As a result, half of COPD deterioration remains undetected. The proposed technology facilitates early diagnosis and treatment of COPD attacks by continuous remote tracking of lung function, thereby preventing unnecessary ER visits and hospitalizations. This device improves care for long-term conditions like COPD or asthma, but also those with relatively quick onset, such as COVID-19.
The proposed project advances translation of a device using acoustic resonance to measure lung air volume. The project develops an expanded data set for analysis and deployment at scale. The project further incorporates advanced classification models into the analysis workflow to track and predict deteriorating lung function in a cloud-computing environment. The project will also develop a version to run on a patient's mobile device to enable real-time feedback.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
The proposed project advances translation of a device using acoustic resonance to measure lung air volume. The project develops an expanded data set for analysis and deployment at scale. The project further incorporates advanced classification models into the analysis workflow to track and predict deteriorating lung function in a cloud-computing environment. The project will also develop a version to run on a patient's mobile device to enable real-time feedback.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.