University of Pittsburgh scientists have developed a pulmonary telemedicine device (PTEase) that can be used remotely by patients to accurately measure the cross-sectional area (CSA) of each airway segment. Using only a commercial smartphone and a novel attachment (Ref: ID 6140) an algorithm has been designed to use acoustic signals and sensing to determine the internal physiology of the patient’s airways and allow for the prediction and monitoring of pulmonary diseases.
Description
The COVID-19 pandemic highlighted the clinical need for remote monitoring and evaluation of pulmonary disease. During the height of the pandemic, many patients would avoid hospital settings and, given COVID-19 is a highly infectious respiratory virus, safer and more accurate methods to assess lung function were required. PTEase has been developed to allow for remote monitoring of pulmonary function without the need for advanced training or expensive specialist equipment. This system could improve access to monitoring, improving patient outcomes through earlier diagnosis and regular monitoring.
Applications
• Cystic fibrosis
• Asthma
• Pulmonary diseases
Advantages
Current approaches to diagnosis and monitoring pulmonary disease often involve subjective reporting from patients, who may fail to recognize early small symptoms or slow decline in lung function leading to acute emergencies. Current pulmonary function tests require specialist equipment, are costly (e.g., spirometry > $50,000) and aerosol-generating, so not suitable during a highly infectious respiratory pandemic.
This novel application overcomes these challenges. Designed as an app for commercially available smartphones and an easy-to-use mouthpiece, PTEase uses active acoustic sensing and machine learning (ML) to measure changes in the lower airways. Designed to release signals through the phone’s speakers via a mouthpiece, signal reflections are analyzed, and the internal CSA is determined using ML. PTEase also includes calibration steps to reduce false signals from echoes and imbalanced gains in the phone’s microphone or speakers increasing the signal-to-noise ratio.
Invention Readiness
Software and a working prototype have been developed. Testing in lab-controlled environments on 3D printed airways demonstrated accuracy within 10%, including across different smartphone models. A small study in humans showed PTEase could accurately predict lung function with 11–15% accuracy and demonstrated 75% accuracy in predicting pulmonary disease. Work is required to achieve better sensing accuracy, validate the algorithm in larger groups and improve prediction.
IP Status
Patent pending
