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Pulmonary Fibrosis Lung Sounds Study

Not yet recruiting
Conditions
Pulmonary Fibrosis
Healthy
Interventions
Device: Stemoscope (bluetooth sound amplifier)
Registration Number
NCT05771740
Lead Sponsor
Royal Devon and Exeter NHS Foundation Trust
Brief Summary

The goal of this observational study is to test whether it is possible to detect particular lung sounds that are unique to patients with the lung disease pulmonary fibrosis and whether any such sounds could be analysed using machine learning to make diagnosing disease easier.

Participants will have a sound detection device placed in different locations on the chest and audio sounds will be recorded for analysis.

Researchers will compare audio recordings from clinically diagnosed patients with recordings from healthy controls of a similar age to see whether the sounds are sufficiently different within that age group.

Detailed Description

This is a study of chest audio recordings obtained using a sound enhancer, in this case a Bluetooth device, combined with intelligent computer-processing and analysis. It is being carried out amongst pulmonary fibrosis patients and healthy controls of a similar age, with the aim to improve diagnosis of pulmonary fibrosis and remote monitoring of disease progression.

Expert respiratory doctors gain important insights about the health of a patient's lungs by listening to the chest with a stethoscope. Currently, there are insufficient respiratory experts and specialist equipment to meet the patient demand, leading to delays in diagnosis and treatment and a shortage of specialist care following diagnosis.

In this study the investigators are aiming to make that specialist practice much more available by recording lung sounds and developing software to do the intelligent analysis. Initial tests with publicly available recordings of expertly diagnosed respiratory sounds have shown that different lung diseases can be detected with a very high degree of accuracy using new software. Here the investigators want to test that software with a cost-effective digital sound device in a clinical setting. The aim is for respiratory diseases to be diagnosed quickly and easily and also, in future, for patients to be offered the option to monitor how well they are after diagnosis in their own home.

Recruitment & Eligibility

Status
NOT_YET_RECRUITING
Sex
All
Target Recruitment
100
Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
Pulmonary Fibrosis PatientStemoscope (bluetooth sound amplifier)Participants under the clinical care of the interstitial lung disease team at the Royal Devon University Healthcare NHS Trust, UK
Healthy ControlStemoscope (bluetooth sound amplifier)Healthy participants visiting the Royal Devon University Healthcare NHS Trust, UK
Primary Outcome Measures
NameTimeMethod
Measure of ability of this system to classify participants as PF patients or healthy controls8 months

A measure of the capability of the machine learning model combined with the cost-effective bluetooth stethoscope to classify study participants as PF patients or healthy controls from lung sound recordings alone in a clinical setting

Feedback from patients and study clinicians8 months

Feedback from patients and study clinicians about the acceptability of digital sound monitoring for improving future diagnosis and monitoring of disease progression in pulmonary fibrosis

Number of clinical lung sound recordings stored from pulmonary fibrosis cases and controls6 months

A measure of the feasibility of gathering 12 lung sound files from each of 50 PF patients and 50 healthy volunteers in a similar age-group in the available timeframe.

Secondary Outcome Measures
NameTimeMethod
A correlation between clinical measures of pulmonary fibrosis severity and the audio waveform6 months

A demonstrable correlation between objective markers of pulmonary function tests (forced vital capacity percent of predicted (FVC%) or diffusing capacity in the lung for carbon monoxide percentage predicted (DLCO%)) or breathlessness symptoms (mMRC Dyspnoea score) and the waveform of the audio recording.

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