Validity of a deep learning algorithm for detecting wheezes and crackles from lung sound recordings in adults

We validated our state-of-the-art deep learning algorithm for detection of wheezes and crackles in sound files by comparing the classification of our algorithm with those of human experts. We had two validation sets classified by experienced raters that were not used to train the algorithm with 615 (A) and 120 (B) sound files, respectively. We calculated Area Under Curve (AUC) of the algorithm’s probability scores for wheezes and crackles. We dichotomized the scores and calculated sensitivity and specificity as well as kappa agreement. In set A, the AUC was 0.88 (95% CI 0.84 – 0.92) for wheezes and 0.88 (95% CI 0.84 – 0.92) for crackles. The sensitivities and specificities of the labels were 81% and 89% for wheezes and 67% and 96% for crackles. In set B, the kappa agreement between the algorithm and the validation set was 0.78 (95% CI 0.58 – 0.99) for wheezes and 0.75 (95% CI 0.59 – 0.92) for crackles. The 24 observers who had rated the same 120 sound files agreed less with the reference classification with a mean kappa of 0.68 for wheezes and 0.55 for crackles. We found the algorithm to be superior to doctors in detecting wheezes and crackles in lung sound files. ### Competing Interest Statement The results of this work will be used by the Medsensio AS company where JR is CTO. JR and LAB have shares in Medsensio AS. MP is an employee in Medsensio AS. HM and JCAS have done paid work for Medsensio AS. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Regional Ethical Committee of North Norway gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Researchers can apply for access to the The Tromsø Study data at: