Analysis of pulmonary sounds for the diagnosis of interstitial lung diseases secondary to rheumatoid arthritis

In this study, we investigate the problem of the automatic detection of velcro crackle in lung sounds. In practice, the patient is auscultated using a digital stethoscope and the lung sounds are saved to a file. The acquired digital data are then analysed using a suitably developed algorithm. In particular, the proposed solution relies on the empirical observation that the audio bandwidth associated with velcro crackle is larger than that associated with healthy breath sounds. Experimental results from a database of 70 patients affected by rheumatoid arthritis demonstrate that the developed tool can outperform specialized physicians in terms of diagnosing pulmonary disorders. The overall accuracy of the proposed solution is 90.0%, with negative and positive predictive values of 95.0% and 83.3%, respectively, whereas the reliability of physician diagnosis is in the range of 60−70%. The devised algorithm represents an enabling technology for a novel approach to the diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis.