Frequency-time Analysis of Pathological Lung Sounds: Detection and Quantification of Pathological Sounds in Patients With Cystic Fibrosis, Pulmonary Fibrosis or COPD (Chronic Obstructive Pulmonary Disease)

Chronic Obstructive Pulmonary Disease Clinical Trial

— ATF/SPP  

Official title:

Frequency-time Analysis of Pathological Lung Sounds: Detection and Quantification of Pathological Sounds in Patients With Cystic Fibrosis, Pulmonary Fibrosis or COPD (Chronic Obstructive Pulmonary Disease)

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06399094
• Other study ID #: GHRMSA 1315
• Status: Not yet recruiting
• Start date: June 1, 2024
• Est. completion date: January 1, 2025

Study information

• Verified date: April 2024
• Source: Groupe Hospitalier de la Region de Mulhouse et Sud Alsace
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The main objective of the study is to assess the potential of time-frequency representation and analysis of pulmonary sounds collected with an electronic stethoscope, as part of the routine monitoring of patients with cystic fibrosis, COPD or pulmonary fibrosis.

Description:

Secondary objectives

The other objectives of this study are :

1. To evaluate the ability to detect changes in lung sounds, following optimization of the time-frequency representation.

2. To evaluate the ability to quantify differences in the severity of the pathological sounds detected using artificial intelligence and a supervised learning method.

Conduct of research This is a single-center, non-randomized, open-label study involving 60 male and female patients aged 18 to 65, eligible for a scheduled consultation as part of their usual pathological follow-up (routine care).

Lung sound recordings will be made during the same consultation, after obtaining the patient's non-opposition.

Recordings will be made using a 3M Littmann© model 3200 electronic stethoscope. The stethoscope works with Eko software, which will be installed on a touch-sensitive tablet or computer, enabling local storage of recorded data.

Whatever the patient's pathology, the physician will listen to 10 lung sites defined in the protocol. At least one breath per pulmonary site will be recorded during the consultation.

If a patient comes back for a consultation before the end of the recruitment period, a new lung sound recording will be performed.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 60
• Est. completion date: January 1, 2025
• Est. primary completion date: January 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Age = 18 years

• Suffering from one of the following pathologies: cystic fibrosis, pulmonary fibrosis, COPD

• Not opposed to participating in the study

Exclusion Criteria:

• Person under court protection, guardianship or curatorship

• Person deprived of liberty by judicial or administrative decision

• Patient with a history of thoracic surgery, thoracic deformity, heart failure or other relevant illness at the investigator's discretion.

Related Conditions & MeSH terms

• Chronic Obstructive Pulmonary Disease
• Cystic Fibrosis
• Fibrosis
• Lung Diseases
• Lung Diseases, Obstructive
• Pulmonary Disease, Chronic Obstructive
• Pulmonary Fibrosis
• Respiratory Sounds

Intervention

Other:
• Lung sound recordings
The lung sound recordings will be made during a consultation scheduled as part of the patient's usual follow-up, after obtaining the patient's non-opposition.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Groupe Hospitalier de la Region de Mulhouse et Sud Alsace

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Lung sounds visible in their representation as time-frequency images	Pulmonary sounds will be recorded with an electronic stethoscope at each scheduled visit and processed with artificial intelligence using a supervised learning method.	At inclusion
Secondary	Classification of sounds by severity	Pulmonary sounds will be recorded with an electronic stethoscope at each scheduled visit and processed with artificial intelligence using a supervised learning method.	At inclusion