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Deep Learning Diagnostic and Risk-stratification for IPF and COPD
Sponsor: Pediatric Clinical Research Platform
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.
Official title: Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
160
Start Date
2023-04-01
Completion Date
2024-10-31
Last Updated
2024-04-12
Healthy Volunteers
No
Conditions
Interventions
Lung auscultation
Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).
Lung ultrasound
Lung ultrasonography
Quality of Life's questionnaires
Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)
Pulmonary functional tests
Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.
Locations (1)
Centre Hospitalier du Valais Romand
Sion, Valais, Switzerland