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.

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.

Inclusion Criteria

• Able to understand what the study involves
• Able and willing to give informed consent

For patients:

• Age≥60 (PF affects adults, with the majority of age≥60)
• A diagnosis of progressive pulmonary fibrosis (to include all diagnosed interstitial lung disease (ILD) patients presenting at the ILD clinic)

For healthy controls:

• Age≥60 (to ensure that age of controls is similar to that of patients so that younger age does not bias results)
• No known lung disease

Exclusion Criteria

• Unable to understand what the study involves
• Unwilling or unable to give consent
• Age<60 (to ensure that age does not confound results and cases and controls are of similar age, since the majority of pulmonary fibrosis patients are of age≥60)

50 Patients with a diagnosis of progressive pulmonary fibrosis attending routine interstitial lung disease clinic (age≥60) 50 Healthy controls of a similar age range (age≥60)

Individual participant data will be available on an anonymised basis via Open Research Exeter data depository (https://ore.exeter.ac.uk/repository).

In accordance with MRC data sharing policy, the anonymous electronic data will be stored indefinitely and will be available for sharing for research purposes on request after 2 years from receipt of the full dataset.

Available on request as above