Deep Learning Diagnostic and Risk-stratification for IPF and COPD — DeepBreath  

Pulmonary Disease, Chronic Obstructive Clinical Trial

Official title: Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations

Status Recruiting

Clinical Trial 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.

Clinical Trial Description

Aim: To develop and determine the predictive power of an AI (deep learning) algorithm in identifying the acoustic and LUS signatures of IPF, NSIP and COPD in an adult population and discriminating them from age-matched, never smoker, control subjects with normal lung function. Methodology: A single-center, prospective, population-based case-control study that will be carried out in subjects with IPF, NSIP and COPD. A total of 120 consecutive patients aged ≥ 18 years and meeting IPF, NSIP or COPD international criteria, and 40 age-matched controls, will be recruited in a Swiss pulmonology outpatient clinic with a total of approximately 7000 specialized consultations per year, starting from August 2022. At inclusion, demographic and clinical data will be collected. Additionally, lung auscultation will be recorded with a digital stethoscope and LUS performed. A deep learning algorithm (DeepBreath) using various deep learning networks with aggregation strategies will be trained on these audio recordings and lung images to derive an automated prediction of diagnostic (i.e., positive vs negative) and risk stratification categories (mild to severe). Secondary outcomes will be to measures the association of analysed lung sounds with clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Patients' quality of life will be measured with the standardized dedicated King's Brief Interstitial Lung Disease (K-BILD) and the COPD assessment test (CAT) questionnaires. Expected results: This study seeks to explore the synergistic value of several point-of-care-tests for the detection and differential diagnosis of ILD and COPD as well as estimate severity to better guide care management in adults ;

Related Conditions & MeSH terms

• Artificial Intelligence
• Chronic Disease
• Fibrosis
• Idiopathic Pulmonary Fibrosis
• Lung Diseases
• Lung Diseases, Interstitial
• Lung; Disease, Interstitial, With Fibrosis
• Pulmonary Disease, Chronic Obstructive

• NCT number: NCT05318599
• Study type: Observational
• Source: Pediatric Clinical Research Platform
• Contact: Johan N. Siebert, MD
• Phone: +41795534072
• Email: Johan.Siebert@hcuge.ch
• Status: Recruiting
• Start date: April 1, 2023
• Completion date: October 31, 2024