Early detection of Pulmonary fibrosis by analysis of breathing sounds

Pulmonary fibrosis comprises more than 200 different disorders that are all characterized by a variable mixture of inflammation and fibrosis. Unfortunately there is no cure for these diseases, but for these diseases where fibrosis is the driver of the disease there are specific antifibrotic agents that stop or slow down further progression of the disease. Therefor early detection is the only way to initiate an effective treatment for our patients. Another issue in these disorders is the lack of guidance on what processes drive these disorders being it mainly inflammation or fibrosis. This is crucial as for the diseases that are comprised in the progressive fibrosis concept antifibrotics might be the first choice of treatment. Another hurdle is the prediction whether in an individual patient the disease will be stable or progressive. The field struggles for more than 10 years with these issues and it is clear that we lack important biomarkers to solve these problems. Even if we combine clinical data with imaging and tissue we are not able to answer all the former questions. Therefore it is urgent to come up with a new approach which will add data to those that are already known and systematically collected. An added value would be if this additional test would be non invasive, highly standardizable and with a low interobserver variation. We think that the analysis of the breathing sounds in patients with IPF would be an alternative approach that might help us to solve the former issues Our hypothesis would be that by using breathing sounds of patients with IPF we would be able to: 1. to detect early pulmonary fibrosis (IPF or non-ILD) based on 1 recording 2. to identify whether inflammation or fibrosis is the main driver of the disease process in the individual patient.