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Pulmonary diseases, such as asthma and Chronic Obstructive Pulmonary Disease (COPD), constitute a major public health challenge. The disease symptoms, including airway obstruction and inflammation, usually result in changes in airway mechanical properties, such as the caliber and impedance of the airway. To measure such airway properties for disease evaluation and diagnosis purposes, pulmonary function tests (PFT) has been widely adopted. However, most existing PFT systems require expensive and cumbersome hardware that are impossible to be used out of clinic. To allow out-clinic continuous pulmonary disease evaluation, in this paper we present AWARE, a new sensing and AI system that supports accurate and reliable PFT using commodity smartphones. AWARE uses a smartphone to transmit acoustic signals and reconstructs the profile of human airway based on the analysis of reflected acoustic waves captured from the smartphone's microphone. The subject's pulmonary condition is then evaluated by a multi-task learning model that integrates both the airway measurements and the subject's lung function records as the ground truth. Evaluations on 75 human subjects demonstrate that AWARE has the capability to achieve 80% accuracy on distinguishing between humans with healthy pulmonary function and with asthma symptoms.
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Early Diagnosis and Real-Time Monitoring of Regional Lung Function Changes to Prevent Chronic Obstructive Pulmonary Disease Progression to Severe Emphysema
First- and second-hand exposure to smoke or air pollutants is the primary cause of chronic obstructive pulmonary disease (COPD) pathogenesis, where genetic and age-related factors predispose the subject to the initiation and progression of obstructive lung disease. Briefly, airway inflammation, specifically bronchitis, initiates the lung disease, leading to difficulty in breathing (dyspnea) and coughing as initial symptoms, followed by air trapping and inhibition of the flow of air into the lungs due to damage to the alveoli (emphysema). In addition, mucus obstruction and impaired lung clearance mechanisms lead to recurring acute exacerbations causing progressive decline in lung function, eventually requiring lung transplant and other lifesaving interventions to prevent mortality. It is noteworthy that COPD is much more common in the population than currently diagnosed, as only 16 million adult Americans were reported to be diagnosed with COPD as of 2018, although an additional 14 million American adults were estimated to be suffering from COPD but undiagnosed by the current standard of care (SOC) diagnostic, namely the spirometry-based pulmonary function test (PFT). Thus, the main issue driving the adverse disease outcome and significant mortality for COPD is lack of timely diagnosis in the early stages of the disease. The current treatment regime for COPD emphysema is most effective when implemented early, on COPD onset, where alleviating symptoms and exacerbations with timely intervention(s) can prevent steep lung function decline(s) and disease progression to severe emphysema. Therefore, the key to efficiently combatting COPD relies on early detection. Thus, it is important to detect early regional pulmonary function and structural changes to monitor modest disease progression for implementing timely interventions and effectively eliminating emphysema progression. Currently, COPD diagnosis involves using techniques such as COPD screening questionnaires, PFT, arterial blood gas analysis, and/or lung imaging, but these modalities are limited in their capability for early diagnosis and real-time disease monitoring of regional lung function changes. Hence, promising emerging techniques, such as X-ray phase contrast, photoacoustic tomography, ultrasound computed tomography, electrical impedance tomography, the forced oscillation technique, and the impulse oscillometry system powered by robust artificial intelligence and machine learning analysis capability are emerging as novel solutions for early detection and real time monitoring of COPD progression for timely intervention. We discuss here the scope, risks, and limitations of current SOC and emerging COPD diagnostics, with perspective on novel diagnostics providing real time regional lung function monitoring, and predicting exacerbation and/or disease onset for prognosis-based timely intervention(s) to limit COPD–emphysema progression.
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- Award ID(s):
- 2112092
- PAR ID:
- 10396870
- Date Published:
- Journal Name:
- Journal of Clinical Medicine
- Volume:
- 10
- Issue:
- 24
- ISSN:
- 2077-0383
- Page Range / eLocation ID:
- 5811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/jcm10245811
