Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.

Background: Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers.

Methods: Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study.

Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection.

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings.