SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a next-generation product that will revolutionize COPD management by empowering patients, providers and caregivers to monitor the disease, prevent respiratory attacks, and receive timely care at home. Currently, COPD kills one American every four minutes and costs nearly $72B a year - almost half of that cost is attributable to ER visits and hospitalization. Projected benefits include early detection of lung deterioration, which will facilitate preventive interventions at home and thereby reduce the $36B/year spent on ER and hospital visits. A recent CMS regulatory change (CMS-1689-FC) permits reimbursements for remote monitoring of COPD patients, indicating market readiness by recognizing the success of remote monitoring in reducing admissions and long-term acute care use. Our solution has a validated business model (through over 220 I-Corps customer interviews) that drives value for patients, physicians, provider networks and payers. Successful development of this product is forecast to create 42 new jobs (2024) with an annual payroll exceeding $6.5M. As a direct result of this award, this innovative product can reach the U.S. market in 2023, with $100M in projected revenue by 2027. This Small Business Innovation Research (SBIR) Phase I project aims to develop a new gold-standard for detecting COPD deterioration. The product is based on low-cost audio sensors paired with AI algorithms on a Smartphone platform that track lung resonance and flag any changes in lung volume. Current methods for tracking lung function at home are sub-optimal. Home spirometers are difficult to use, and not reimbursable. Pulse-oximeters are highly inaccurate and only provide data at discrete points, leading to late diagnosis. This lack of timely information manifests in excess hospitalizations because detection often occurs too late to prevent an attack. This product creates a fundamental shift in the technology employed at home by identifying air trapping, a more sensitive biomarker for lung deterioration. The novelty is affirmed with two pending patent applications and a freedom to operate analysis that found no prior art of concern. Currently lung resonance measurement is not used in other respiratory monitoring devices, providing the company with a strong competitive differentiation. The goal of this project is to create a Minimum Viable Product that can be used for human testing. By the end of this award the company will test and validate the concept in a small cohort of patients and controls. 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 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.