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. 2024 Apr 24;25(1):177.
doi: 10.1186/s12931-024-02810-5.

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

Eline Lauwers  1   2 , Toon Stas  3 , Ian McLane  4   5 , Annemiek Snoeckx  6   7 , Kim Van Hoorenbeeck  8   9 , Wilfried De Backer  7   10   11 , Kris Ides  8   3   9   11 , Jan Steckel  3 , Stijn Verhulst  8   9
Affiliations

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

Eline Lauwers et al. Respir Res. .

Abstract

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. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level.

Results: 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording.

Conclusions: The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Keywords: Chest computed tomography; Computer aided lung sound analysis; Cystic fibrosis; Digital lung auscultation; Functional respiratory imaging.

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Conflict of interest statement

IM was Chief Technology Officer at Sonavi Labs at the time the study was executed, which is a medical device and software company that commercialized an advanced digital stethoscope to record and analyze lung sounds using AI technology. WDB is executive chair of the board of directors at Fluidda NV, and EL is an employee of Fluidda NV, a company that develops and markets the FRI technology described in this paper. The other authors had no financial relationships with any organization or company that might have an interest in the submitted work during the conduct of this study. No direct funding was received from either Sonavi Labs or Fluidda NV.

Figures

Fig. 1
Fig. 1
A-H. Lung sound characteristics vs. CF-CT scores with the regression lines of the mixed-effects models. E/I, expiration-to-inspiration signal power ratio
Fig. 2
Fig. 2
A-D. Lung sound characteristics vs. FRI parameters with the regression lines of the mixed-effects models. E/I, expiration-to-inspiration signal power ratio; iVaw, airway volume; iRaw, airway resistance
See this image and copyright information in PMC

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