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Remote monitoring and evaluation of pulmonary diseases via telemedicine are important to disease diagnosis and management, but current telemedicine solutions have limited capability of objectively examining the airway's internal physiological conditions that are crucial to pulmonary disease evaluation. Existing solutions based on smartphone sensing are also limited to externally monitoring breath rates, respiratory events, or lung function. In this paper, we present PTEase, a new system design that addresses these limitations and uses commodity smartphones to examine the airway's internal physiological conditions. PTEase uses active acoustic sensing to measure the internal changes of lower airway caliber, and then leverages machine learning to analyze the sensory data for pulmonary disease evaluation. We implemented PTEase as a smartphone app, and verified its measurement error in lab-controlled settings as <10%. Clinical studies further showed that PTEase reaches 75% accuracy on disease prediction and 11%-15% errors in estimating lung function indices. Given that such accuracy is comparable with that in clinical practice using spirometry, PTEase can be reliably used as an assistive telemedicine tool for disease evaluation and monitoring.
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Future Trends in Semiconducting Gas-Selective Sensing Probes for Skin Diagnostics
This paper presents sensor nanotechnologies that can be used for the skin-based gas “smelling” of disease. Skin testing may provide rapid and reliable results, using specific “fingerprints” or unique patterns for a variety of diseases and conditions. These can include metabolic diseases, such as diabetes and cholesterol-induced heart disease; neurological diseases, such as Alzheimer’s and Parkinson’s; quality of life conditions, such as obesity and sleep apnea; pulmonary diseases, such as cystic fibrosis, asthma, and chronic obstructive pulmonary disease; gastrointestinal tract diseases, such as irritable bowel syndrome and colitis; cancers, such as breast, lung, pancreatic, and colon cancers; infectious diseases, such as the flu and COVID-19; as well as diseases commonly found in ICU patients, such as urinary tract infections, pneumonia, and infections of the blood stream. Focusing on the most common gaseous biomarkers in breath and skin, which is nitric oxide and carbon monoxide, and certain abundant volatile organic compounds (acetone, isoprene, ammonia, alcohols, sulfides), it is argued here that effective discrimination between the diseases mentioned above is possible, by capturing the relative sensor output signals from the detection of each of these biomarkers and identifying the distinct breath print for each disease.
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- Award ID(s):
- 2014506
- PAR ID:
- 10358039
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 22
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 7554
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21227554
