Deep Learning Diagnostic and Risk-stratification for IPF and COPD
Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
Pediatric Clinical Research Platform
160 participants
Apr 1, 2023
OBSERVATIONAL
Conditions
Summary
Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.
Eligibility
Plain Language Summary
Simplified for easier understanding
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Interventions
Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).
Lung ultrasonography
Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)
Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.
Locations(1)
View Full Details on ClinicalTrials.gov
For the most up-to-date information, visit the official listing.
NCT05318599